Publications by authors named "Hongkai Xiong"

29 Publications

  • Page 1 of 1

Automatic Image Selection Model Based on Machine Learning for Endobronchial Ultrasound Strain Elastography Videos.

Front Oncol 2021 31;11:673775. Epub 2021 May 31.

School of Electronic Information & Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Background: Endoscopic ultrasound (EBUS) strain elastography can diagnose intrathoracic benign and malignant lymph nodes (LNs) by reflecting the relative stiffness of tissues. Due to strong subjectivity, it is difficult to give full play to the diagnostic efficiency of strain elastography. This study aims to use machine learning to automatically select high-quality and stable representative images from EBUS strain elastography videos.

Methods: LNs with qualified strain elastography videos from June 2019 to November 2019 were enrolled in the training and validation sets randomly at a quantity ratio of 3:1 to train an automatic image selection model using machine learning algorithm. The strain elastography videos in December 2019 were used as the test set, from which three representative images were selected for each LN by the model. Meanwhile, three experts and three trainees selected one representative image severally for each LN on the test set. Qualitative grading score and four quantitative methods were used to evaluate images above to assess the performance of the automatic image selection model.

Results: A total of 415 LNs were included in the training and validation sets and 91 LNs in the test set. Result of the qualitative grading score showed that there was no statistical difference between the three images selected by the machine learning model. Coefficient of variation (CV) values of the four quantitative methods in the machine learning group were all lower than the corresponding CV values in the expert and trainee groups, which demonstrated great stability of the machine learning model. Diagnostic performance analysis on the four quantitative methods showed that the diagnostic accuracies were range from 70.33% to 73.63% in the trainee group, 78.02% to 83.52% in the machine learning group, and 80.22% to 82.42% in the expert group. Moreover, there were no statistical differences in corresponding mean values of the four quantitative methods between the machine learning and expert groups (p >0.05).

Conclusion: The automatic image selection model established in this study can help select stable and high-quality representative images from EBUS strain elastography videos, which has great potential in the diagnosis of intrathoracic LNs.
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http://dx.doi.org/10.3389/fonc.2021.673775DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201408PMC
May 2021

K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations.

IEEE Trans Pattern Anal Mach Intell 2021 May 21;PP. Epub 2021 May 21.

In this paper, we propose the K-Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual instances. It aims to combine the advantages of {\em inter-instance discrimination} by learning discriminative features to distinguish between different instances, as well as {\em intra-instance variations} by matching queries against the variants of augmented samples over instances. Particularly, for each instance, it constructs an instance subspace to model the configuration of how the significant factors of variations in K-shot augmentations can be combined to form the variants of augmentations. Given a query, the most relevant variant of instances is then retrieved by projecting the query onto their subspaces to predict the positive instance class. This generalizes the existing contrastive learning that can be viewed as a special one-shot case. An eigenvalue decomposition is performed to configure instance subspaces, and the embedding network can be trained end-to-end through the differentiable subspace configuration. Experiment results demonstrate the proposed K-shot contrastive learning achieves superior performances to the state-of-the-art unsupervised methods.
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http://dx.doi.org/10.1109/TPAMI.2021.3082567DOI Listing
May 2021

General Bitwidth Assignment for Efficient Deep Convolutional Neural Network Quantization.

IEEE Trans Neural Netw Learn Syst 2021 Apr 8;PP. Epub 2021 Apr 8.

Model quantization is essential to deploy deep convolutional neural networks (DCNNs) on resource-constrained devices. In this article, we propose a general bitwidth assignment algorithm based on theoretical analysis for efficient layerwise weight and activation quantization of DCNNs. The proposed algorithm develops a prediction model to explicitly estimate the loss of classification accuracy led by weight quantization with a geometrical approach. Consequently, dynamic programming is adopted to achieve optimal bitwidth assignment on weights based on the estimated error. Furthermore, we optimize bitwidth assignment for activations by considering the signal-to-quantization-noise ratio (SQNR) between weight and activation quantization. The proposed algorithm is general to reveal the tradeoff between classification accuracy and model size for various network architectures. Extensive experiments demonstrate the efficacy of the proposed bitwidth assignment algorithm and the error rate prediction model. Furthermore, the proposed algorithm is shown to be well extended to object detection.
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http://dx.doi.org/10.1109/TNNLS.2021.3069886DOI Listing
April 2021

iPool--Information-Based Pooling in Hierarchical Graph Neural Networks.

IEEE Trans Neural Netw Learn Syst 2021 Mar 31;PP. Epub 2021 Mar 31.

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspective. The majority of these works, however, focus on adapting the convolution operator to graph representation. At the same time, the pooling operator also plays an important role in distilling multiscale and hierarchical representations, but it has been mostly overlooked so far. In this article, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs. With the argument that informative nodes dominantly characterize graph signals, we propose a criterion to evaluate the amount of information of each node given its neighbors and theoretically demonstrate its relationship to neighborhood conditional entropy. This new criterion determines how nodes are selected and coarsened graphs are constructed in the pooling layer. The resulting hierarchical structure yields an effective isomorphism-invariant representation of networked data on arbitrary topologies. The proposed strategy achieves superior or competitive performance in graph classification on a collection of public graph benchmark data sets and superpixel-induced image graph data sets.
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http://dx.doi.org/10.1109/TNNLS.2021.3067441DOI Listing
March 2021

Partially-Connected Neural Architecture Search for Reduced Computational Redundancy.

IEEE Trans Pattern Anal Mach Intell 2021 09 4;43(9):2953-2970. Epub 2021 Aug 4.

Differentiable architecture search (DARTS) enables effective neural architecture search (NAS) using gradient descent, but suffers from high memory and computational costs. In this paper, we propose a novel approach, namely Partially-Connected DARTS (PC-DARTS), to achieve efficient and stable neural architecture search by reducing the channel and spatial redundancies of the super-network. In the channel level, partial channel connection is presented to randomly sample a small subset of channels for operation selection to accelerate the search process and suppress the over-fitting of the super-network. Side operation is introduced for bypassing (non-sampled) channels to guarantee the performance of searched architectures under extremely low sampling rates. In the spatial level, input features are down-sampled to eliminate spatial redundancy and enhance the efficiency of the mixed computation for operation selection. Furthermore, edge normalization is developed to maintain the consistency of edge selection based on channel sampling with the architectural parameters for edges. Theoretical analysis shows that partial channel connection and parameterized side operation are equivalent to regularizing the super-network on the weights and architectural parameters during bilevel optimization. Experimental results demonstrate that the proposed approach achieves higher search speed and training stability than DARTS. PC-DARTS obtains a top-1 error rate of 2.55 percent on CIFAR-10 with 0.07 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.1 percent on ImageNet (under the mobile setting) within 2.8 GPU-days.
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http://dx.doi.org/10.1109/TPAMI.2021.3059510DOI Listing
September 2021

Deep learning with convex probe endobronchial ultrasound multimodal imaging: A validated tool for automated intrathoracic lymph nodes diagnosis.

Endosc Ultrasound 2021 Feb 9. Epub 2021 Feb 9.

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Background And Objectives: Along with the rapid improvement of imaging technology, convex probe endobronchial ultrasound (CP-EBUS) sonographic features play an increasingly important role in the diagnosis of intrathoracic lymph nodes (LNs). Conventional qualitative and quantitative methods for EBUS multimodal imaging are time-consuming and rely heavily on the experience of endoscopists. With the development of deep-learning (DL) models, there is great promise in the diagnostic field of medical imaging.

Materials And Methods: We developed DL models to retrospectively analyze CP-EBUS images of 294 LNs from 267 patients collected between July 2018 and May 2019. The DL models were trained on 245 LNs to differentiate benign and malignant LNs using both unimodal and multimodal CP-EBUS images and independently evaluated on the remaining 49 LNs to validate their diagnostic efficiency. The human comparator group consisting of three experts and three trainees reviewed the same test set as the DL models.

Results: The multimodal DL framework achieves an accuracy of 88.57% (95% confidence interval [CI] [86.91%-90.24%]) and area under the curve (AUC) of 0.9547 (95% CI [0.9451-0.9643]) using the three modes of CP-EBUS imaging in comparison to the accuracy of 80.82% (95% CI [77.42%-84.21%]) and AUC of 0.8696 (95% CI [0.8369-0.9023]) by experts. Statistical comparison of their average receiver operating curves shows a statistically significant difference (P < 0.001). Moreover, the multimodal DL framework is more consistent than experts (kappa values 0.7605 vs. 0.5800).

Conclusions: The DL models based on CP-EBUS imaging demonstrated an accurate automated tool for diagnosis of the intrathoracic LNs with higher diagnostic efficiency and consistency compared with experts.
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http://dx.doi.org/10.4103/EUS-D-20-00207DOI Listing
February 2021

PML-LocNet: Improving Object Localization with Prior-induced Multi-view Learning Network.

IEEE Trans Image Process 2019 Oct 28. Epub 2019 Oct 28.

This paper introduces a new model for Weakly Supervised Object Localization (WSOL) problems where only image-level supervision is provided. The key to solve such problems is to infer the object locations accurately. Previous methods usually model the missing object locations as latent variables, and alternate between updating their estimates and learning a detector accordingly. However, the performance of such alternative optimization is sensitive to the quality of the initial latent variables and the resulted localization model is prone to overfitting to improper localizations. To address these issues, we develop a Prior-induced Multi-view Learning Localization Network (PML-LocNet) which exploits both view diversity and sample diversity to improve object localization. In particular, the view diversity is imposed by a two-phase multi-view learning strategy, with which the complementarity among learned features from different views and the consensus among localized instances from each view are leveraged to benefit localization. The sample diversity is pursued by harnessing coarse-to-fine priors at both image and instance levels. With these priors, more emphasis would go to the reliable samples and the contributions of the unreliable ones would be decreased, such that the intrinsic characteristics of each sample can be exploited to make the model more robust during network learning. PML-LocNet can be easily combined with existing WSOL models to further improve the localization accuracy. Its effectiveness has been proved experimentally. Notably, it achieves 69.3% CorLoc and 50.4% mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.
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http://dx.doi.org/10.1109/TIP.2019.2947155DOI Listing
October 2019

Group Reidentification with Multigrained Matching and Integration.

IEEE Trans Cybern 2021 Mar 17;51(3):1478-1492. Epub 2021 Feb 17.

The task of reidentifying groups of people under different camera views is an important yet less-studied problem. Group reidentification (Re-ID) is a very challenging task since it is not only adversely affected by common issues in traditional single-object Re-ID problems, such as viewpoint and human pose variations, but also suffers from changes in group layout and group membership. In this paper, we propose a novel concept of group granularity by characterizing a group image by multigrained objects: individual people and subgroups of two and three people within a group. To achieve robust group Re-ID, we first introduce multigrained representations which can be extracted via the development of two separate schemes, that is, one with handcrafted descriptors and another with deep neural networks. The proposed representation seeks to characterize both appearance and spatial relations of multigrained objects, and is further equipped with importance weights which capture variations in intragroup dynamics. Optimal group-wise matching is facilitated by a multiorder matching process which, in turn, dynamically updates the importance weights in iterative fashion. We evaluated three multicamera group datasets containing complex scenarios and large dynamics, with experimental results demonstrating the effectiveness of our approach.
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http://dx.doi.org/10.1109/TCYB.2019.2917713DOI Listing
March 2021

Robust Subspace Clustering with Compressed Data.

IEEE Trans Image Process 2019 May 24. Epub 2019 May 24.

Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e.g., clustering) of compressed data. We therefore study in this paper a novel problem called compressive robust subspace clustering, which is to perform robust subspace clustering with the compressed data, and which is generated by projecting the original high-dimensional data onto a lower-dimensional subspace chosen at random. Given only the compressed data and sensing matrix, the proposed method, row space pursuit (RSP), recovers the authentic row space that gives correct clustering results under certain conditions. Extensive experiments show that RSP is distinctly better than the competing methods, in terms of both clustering accuracy and computational efficiency.
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http://dx.doi.org/10.1109/TIP.2019.2917857DOI Listing
May 2019

Joint Pricing and Decision-Making for Heterogeneous User Demand in Cognitive Radio Networks.

IEEE Trans Cybern 2019 Nov 16;49(11):3873-3886. Epub 2018 Jul 16.

The cognitive radio technique allows secondary users (SUs) to share the spectrum with primary users (PUs) in an exclusive or opportunistic manner. This paper studies spectrum pricing conducted by spectrum owners, that is, primary operators (POs), and SU decision-making strategies for three kinds of duopoly markets. The single-band exclusive use market considers two POs with each providing a single band dedicated to SUs. A pre-emptive resume priority (PRP) M/M/1 queueing model is presented, based on which SUs decide to join which PO and which queue. We prove the existence of a unique Wardrop equilibrium for the decision-making process, and a unique Nash equilibrium for the proposed parallel pricing strategy. In a single-band mixed use market, the competition of two POs is represented by a Stackelberg game. We formulate the spectrum sharing among PU and SUs with a 3-level PRP M/M/1 queueing structure, and derive the close form expressions of SUs' queueing delay. In a multiband exclusive use market, where POs have to determine how many bands they will rent as well as the admission price, we define the problem as a mixed integer linear programming problem and propose a global particle swarm optimization algorithm to find the global optimum. Finally, we study a generalized scenario with multiple POs and multiple priority queues.
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http://dx.doi.org/10.1109/TCYB.2018.2851620DOI Listing
November 2019

Multiple Semantic Matching on Augmented $N$ -Partite Graph for Object Co-Segmentation.

IEEE Trans Image Process 2017 Dec 8;26(12):5825-5839. Epub 2017 Sep 8.

Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object co-segmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure. To this end, we first propose to incorporate candidate generation with semantic context. Based on the regions extracted from semantic segmentation of each image, we design a merging mechanism to hierarchically generate candidates with high semantic responses. Second, all candidates are taken into consideration to globally formulate multiple maximum weighted matching cliques, which complement the discovery of part of the common objects induced by a single clique. To facilitate the discovery of multiple matching cliques, an N-partite graph, which inherently excludes intralinks between candidates from the same image, is constructed to separate multiple cliques without additional constraints. Further, we augment the graph with an additional virtual node in each part to handle irrelevant matches when the similarity between the two candidates is too small. Finally, with the explored multiple cliques, we statistically compute pixel-wise co-occurrence map for each image. Experimental results on two benchmark data sets, i.e., iCoseg and MSRC data sets achieve desirable performance and demonstrate the effectiveness of our proposed framework.
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http://dx.doi.org/10.1109/TIP.2017.2750410DOI Listing
December 2017

Progressive Dictionary Learning With Hierarchical Predictive Structure for Low Bit-Rate Scalable Video Coding.

IEEE Trans Image Process 2017 Jun 12;26(6):2972-2987. Epub 2017 Apr 12.

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between the neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a closed-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the state-of-the-art scalable extension of H.264/AVC and latest High Efficiency Video Coding (HEVC), standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest scalable extension of HEVC and HEVC simulcast over extensive test sequences with various resolutions.
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http://dx.doi.org/10.1109/TIP.2017.2692882DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638692PMC
June 2017

Endobronchial Ultrasound Elastography for Evaluation of Intrathoracic Lymph Nodes: A Pilot Study.

Respiration 2017 22;93(5):327-338. Epub 2017 Mar 22.

Department of Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.

Background: Endobronchial ultrasound (EBUS) elastography is a new imaging procedure for describing the elasticity of intrathoracic lesions and providing important additional diagnostic information.

Objectives: The aim of this study was to utilize the feasibility of qualitative and quantitative methods to evaluate the ability of EBUS elastography to differentiate between benign and malignant mediastinal and hilar lymph nodes (LNs) during EBUS-guided transbronchial needle aspiration (EBUS-TBNA).

Methods: Patients with enlarged intrathoracic LNs required for EBUS-TBNA examination at a clinical center for thoracic medicine from January 2014 to April 2014 were prospectively enrolled. EBUS sonographic characteristics on B-mode, vascular patterns and elastography, EBUS-TBNA procedures, pathological findings, and microbiological results were recorded. Furthermore, elastographic patterns (qualitative method) and the mean gray value inside the region of interest (quantitative method) were analyzed. Both methods were compared with a definitive diagnosis of the involved LNs.

Results: Fifty-six patients including 68 LNs (33 benign and 35 malignant nodes) were prospectively enrolled into this study and retrospectively analyzed. Using qualitative and quantitative methods, we were able to differentiate between benign and malignant LNs with high sensitivity, specificity, positive and negative predictive values, and accuracy (85.71, 81.82, 83.33, 84.38, and 83.82% vs. 91.43, 72.73, 78.05, 88.89, and 82.35%, respectively).

Conclusions: EBUS elastography is potentially capable of further differentiating between benign and malignant LNs. These proposed qualitative and quantitative methods might be useful tools for describing EBUS elastography during EBUS-TBNA.
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http://dx.doi.org/10.1159/000464253DOI Listing
October 2017

RGB and HSV quantitative analysis of autofluorescence bronchoscopy used for characterization and identification of bronchopulmonary cancer.

Cancer Med 2016 11 5;5(11):3023-3030. Epub 2016 Oct 5.

Department of Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.

Autofluorescence bronchoscopy (AFB) shows good sensitivity in detecting dysplasia and bronchopulmonary cancer. However, the poor specificity of AFB would lead to excessive biopsy. The aim of the study is to establish a more effective quantitative method (optimal identification index and reference value) for characterizing the AFB images within the region of interest and discuss AFB's significance in the diagnosis of central-type lung cancer. A total of 218 suspected lung cancer patients were enrolled in this study. A quantitative analysis based on color space (red, green, blue[RGB] and HSV system) was conducted and the result was compared with the final diagnosis obtained by the pathology of biopsy. Cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low-grade preinvasive (LGD), high-grade preinvasive (HGD), and invasive cancer. Quantitative analyses in multi-color spaces for the lesions showed by AFB images were conducted by software MATLAB. Finally, there is statistical significance among the different groups in some parameter in RGB and HSV system. So, both RGB and HSV quantitative analysis of autofluorescence bronchoscopy are useful to define benign and malignant diseases, which can objectively guide the bronchoscopist in selecting sites for biopsy with good pathologic correlation.
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http://dx.doi.org/10.1002/cam4.831DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5119956PMC
November 2016

Multitask Learning of Compact Semantic Codebooks for Context-Aware Scene Modeling.

IEEE Trans Image Process 2016 Nov 8;25(11):5411-5426. Epub 2016 Sep 8.

In the past few decades, we have witnessed the success of bag-of-features (BoF) models in scene classification, object detection, and image segmentation. Whereas it is also well acknowledged that the limitation of BoF-based methods lies in the low-level feature encoding and coarse feature pooling. This paper proposes a novel scene classification method, which leverages several semantic codebooks learned in a multitask fashion for robust feature encoding, and designs a context-aware image representation for efficient feature pooling. Apart from conventional universal codebook learning approaches, the proposed method encodes each class of local features with a unique semantic codebook, which captures the distinct distribution of different semantic classes more effectively. Instead of learning each semantic codebook separately, we learn a compact global codebook, of which each semantic codebook is a sparse subset, with a two-stage iterative multitask learning algorithm. While minimizing the clustering divergence, the semantic codeword assignment is solved by submodular optimization simultaneously. Built upon the global and semantic codebooks, a context-aware image representation is further developed to encode both global and semantic features in image representation via contextual quantization, semantic response computation, and semantic pooling. Extensive experiments have been conducted to validate the effectiveness of the proposed method on various public benchmarks with several popular local features.
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http://dx.doi.org/10.1109/TIP.2016.2607424DOI Listing
November 2016

Sparse Representation With Spatio-Temporal Online Dictionary Learning for Promising Video Coding.

IEEE Trans Image Process 2016 Oct 27;25(10):4580-4595. Epub 2016 Jul 27.

Classical dictionary learning methods for video coding suffer from high computational complexity and interfered coding efficiency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3D low-frequency and high-frequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data, such as batch learning methods, e.g., K-SVD. Since the selected volumes are supposed to be independent identically distributed samples from the underlying distribution, decomposition coefficients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL-based coding scheme achieves performance improvements than H.264/AVC or High Efficiency Video Coding as well as existing super-resolution-based methods in rate-distortion performance and visual quality.
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http://dx.doi.org/10.1109/TIP.2016.2594490DOI Listing
October 2016

Application of Quantitative Autofluorescence Bronchoscopy Image Analysis Method in Identifying Bronchopulmonary Cancer.

Technol Cancer Res Treat 2017 08 19;16(4):482-487. Epub 2016 Jul 19.

1 Department of Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.

Autofluorescence bronchoscopy shows good sensitivity and poor specificity in detecting dysplasia and cancer of the bronchus. Through quantitative analysis on the target area of autofluorescence bronchoscopy image, determine the optimal identification index and reference value for identifying different types of diseases and explore the value of autofluorescence bronchoscopy in diagnosis of lung cancer. Patients with 1 or more preinvasive bronchial lesions were enrolled and followed up by white-light bronchoscope and autofluorescence bronchoscopy. Color space quantitative image analysis was conducted on the lesion shown in the autofluorescence image using MATLAB image measurement software. A retrospective analysis was conducted on 218 cases with 1208 biopsies. One hundred seventy-three cases were diagnosed as positive, which included 151 true-positive cases and 22 false-positive cases. White-light bronchoscope associated with autofluorescence bronchoscopy was able to differentiate between benign and malignant lesion with a high sensitivity, specificity, positive predictive value, and negative predictive value (92.1%, 59.3%, 87.3%, and 71.1%, respectively). Taking 1.485 as the cutoff value of receiver operating characteristic of red-to-green value to differentiate benign and malignant diseases, the diagnostic sensitivity reached 82.3% and the specificity reached 80.5%. U values could differentiate invasive carcinoma and other groups well. Quantitative image analysis method of autofluorescence bronchoscopy provided effective scientific basis for the diagnosis of lung cancer and precancerous lesions.
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http://dx.doi.org/10.1177/1533034616656466DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5616066PMC
August 2017

FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption.

BMC Med Inform Decis Mak 2015 21;15 Suppl 5:S5. Epub 2015 Dec 21.

Background: The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment.

Methods: We presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics.

Results: The proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets.

Conclusions: Unlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics.
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http://dx.doi.org/10.1186/1472-6947-15-S5-S5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4698942PMC
October 2016

Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization.

IEEE Trans Image Process 2016 Feb 17;25(2):878-92. Epub 2015 Dec 17.

Fine-grained visual categorization is an emerging research area and has been attracting growing attention recently. Due to the large inter-class similarity and intra-class variance, it is extremely challenging to recognize objects in fine-grained domains. A traditional spatial pyramid matching model could obtain desirable results for the basic-level category classification by weak alignment, but may easily fail in fine-grained domains, since the discriminative features are extremely localized. This paper proposes a new framework for fine-grained visual categorization. First, an efficient part localization method incorporates semantic prior into geometric alignment. It detects the less deformable parts, such as the head of birds with a template-based model, and localizes other highly deformable parts with simple geometric alignment. Second, we learn one-vs-all features, which are simple and transplantable. The learned mid-level features are dimension friendly and more robust to outlier instances. Furthermore, in view that some subcategories are too similar to tell them apart easily, we fuse the subcategories iteratively according to their similarities, and learn fused one-vs-all features. Experimental results show the superior performance of our algorithms over the existing methods.
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http://dx.doi.org/10.1109/TIP.2015.2509425DOI Listing
February 2016

VERTIcal Grid lOgistic regression (VERTIGO).

J Am Med Inform Assoc 2016 05 9;23(3):570-9. Epub 2015 Nov 9.

Department of Biomedical Informatics, UC San Diego, La Jolla, California, USA.

Objective: To develop an accurate logistic regression (LR) algorithm to support federated data analysis of vertically partitioned distributed data sets.

Material And Methods: We propose a novel technique that solves the binary LR problem by dual optimization to obtain a global solution for vertically partitioned data. We evaluated this new method, VERTIcal Grid lOgistic regression (VERTIGO), in artificial and real-world medical classification problems in terms of the area under the receiver operating characteristic curve, calibration, and computational complexity. We assumed that the institutions could "align" patient records (through patient identifiers or hashed "privacy-protecting" identifiers), and also that they both had access to the values for the dependent variable in the LR model (eg, that if the model predicts death, both institutions would have the same information about death).

Results: The solution derived by VERTIGO has the same estimated parameters as the solution derived by applying classical LR. The same is true for discrimination and calibration over both simulated and real data sets. In addition, the computational cost of VERTIGO is not prohibitive in practice.

Discussion: There is a technical challenge in scaling up federated LR for vertically partitioned data. When the number of patients m is large, our algorithm has to invert a large Hessian matrix. This is an expensive operation of time complexity O(m(3)) that may require large amounts of memory for storage and exchange of information. The algorithm may also not work well when the number of observations in each class is highly imbalanced.

Conclusion: The proposed VERTIGO algorithm can generate accurate global models to support federated data analysis of vertically partitioned data.
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http://dx.doi.org/10.1093/jamia/ocv146DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4901373PMC
May 2016

HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS.

Bioinformatics 2016 Jan 6;32(2):211-8. Epub 2015 Oct 6.

Department of Biomedical Informatics, University of California, San Diego, CA 92093.

Motivation: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual's privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size.

Results: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets.

Availability And Implementation: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv563DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4739182PMC
January 2016

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding.

IEEE Trans Circuits Syst Video Technol 2013 Nov;23(11):1941-1956

Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260 USA.

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.
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http://dx.doi.org/10.1109/TCSVT.2013.2269776DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4260422PMC
November 2013

[Quantization Methodology of Autofluorescence Bronchoscopy Image 
in the YUV System].

Zhongguo Fei Ai Za Zhi 2014 Nov;17(11):797-803

Department of Endoscopy Room, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.

Background And Objectives: The aim of this study is to determine the best reference values of the optimal evaluation indexes that identify different disease types. Disease identification was conducted using the YUV quantitative analysis of autofluorescence bronchoscopy (AFB) images in the target areas. Furthermore, this study discusses the significance of AFB in the diagnosis of the central-type lung cancer.

Methods: A biopsy was conducted for cases that showed pathologic changes under either autofluorescence or white-light bronchoscopy. Moreover, MATLAB was used to carry out the quantitative analyses of lesion in multi-color spaces from AFB images. The cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive cancer. SPSS 11.5 was used to process the data for statistical analysis.

Results: The Y values were different and statistically different between invasive cancer and LGD (P<0.001) and invasive cancer and inflammation (P=0.040), respectively. The U values between invasive cancer and the other groups were statistically different (P<0.050). Similarly, the V values between invasive cancer and LGD and inflammation and normal bronchial mucosa were different. Lastly, the V values between normal bronchial mucosa and HGD and inflammation and normal bronchial mucosa were different.

Conclusions: The YUV values in the AFB effectively identified benign and malignant diseases and were proven to be effective scientific bases for the accurate AFB diagnosis of lung cancer.
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http://dx.doi.org/10.3779/j.issn.1009-3419.2014.11.05DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6000354PMC
November 2014

Data-driven hierarchical structure kernel for multiscale part-based object recognition.

IEEE Trans Image Process 2014 Apr;23(4):1765-78

Detecting generic object categories in images and videos are a fundamental issue in computer vision. However, it faces the challenges from inter and intraclass diversity, as well as distortions caused by viewpoints, poses, deformations, and so on. To solve object variations, this paper constructs a structure kernel and proposes a multiscale part-based model incorporating the discriminative power of kernels. The structure kernel would measure the resemblance of part-based objects in three aspects: 1) the global similarity term to measure the resemblance of the global visual appearance of relevant objects; 2) the part similarity term to measure the resemblance of the visual appearance of distinctive parts; and 3) the spatial similarity term to measure the resemblance of the spatial layout of parts. In essence, the deformation of parts in the structure kernel is penalized in a multiscale space with respect to horizontal displacement, vertical displacement, and scale difference. Part similarities are combined with different weights, which are optimized efficiently to maximize the intraclass similarities and minimize the interclass similarities by the normalized stochastic gradient ascent algorithm. In addition, the parameters of the structure kernel are learned during the training process with regard to the distribution of the data in a more discriminative way. With flexible part sizes on scale and displacement, it can be more robust to the intraclass variations, poses, and viewpoints. Theoretical analysis and experimental evaluations demonstrate that the proposed multiscale part-based representation model with structure kernel exhibits accurate and robust performance, and outperforms state-of-the-art object classification approaches.
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http://dx.doi.org/10.1109/TIP.2014.2307480DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5330370PMC
April 2014

Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for Lossless Image Coding.

IEEE Trans Image Process 2014 Feb;23(2):541-54

Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.
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http://dx.doi.org/10.1109/TIP.2013.2293429DOI Listing
February 2014

HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads.

J Am Med Inform Assoc 2014 Mar-Apr;21(2):363-73. Epub 2013 Dec 24.

EE Department, Shanghai Jiaotong University, Shanghai, China.

Background And Objective: Short-read sequencing is becoming the standard of practice for the study of structural variants associated with disease. However, with the growth of sequence data largely surpassing reasonable storage capability, the biomedical community is challenged with the management, transfer, archiving, and storage of sequence data.

Methods: We developed Hierarchical mUlti-reference Genome cOmpression (HUGO), a novel compression algorithm for aligned reads in the sorted Sequence Alignment/Map (SAM) format. We first aligned short reads against a reference genome and stored exactly mapped reads for compression. For the inexact mapped or unmapped reads, we realigned them against different reference genomes using an adaptive scheme by gradually shortening the read length. Regarding the base quality value, we offer lossy and lossless compression mechanisms. The lossy compression mechanism for the base quality values uses k-means clustering, where a user can adjust the balance between decompression quality and compression rate. The lossless compression can be produced by setting k (the number of clusters) to the number of different quality values.

Results: The proposed method produced a compression ratio in the range 0.5-0.65, which corresponds to 35-50% storage savings based on experimental datasets. The proposed approach achieved 15% more storage savings over CRAM and comparable compression ratio with Samcomp (CRAM and Samcomp are two of the state-of-the-art genome compression algorithms). The software is freely available at https://sourceforge.net/projects/hierachicaldnac/with a General Public License (GPL) license.

Limitation: Our method requires having different reference genomes and prolongs the execution time for additional alignments.

Conclusions: The proposed multi-reference-based compression algorithm for aligned reads outperforms existing single-reference based algorithms.
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http://dx.doi.org/10.1136/amiajnl-2013-002147DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932469PMC
May 2014

DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.

PLoS One 2013 25;8(11):e80377. Epub 2013 Nov 25.

Division of Biomedical Informatics, University of California San Diego, La Jolla, California, United States of America ; Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.

Genome data are becoming increasingly important for modern medicine. As the rate of increase in DNA sequencing outstrips the rate of increase in disk storage capacity, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two-pass lossless genome compression algorithm, which highlights the synthesis of complementary contextual models, to improve the compression performance. The proposed framework could handle genome compression with and without reference sequences, and demonstrated performance advantages over best existing algorithms. The method for reference-free compression led to bit rates of 1.720 and 1.838 bits per base for bacteria and yeast, which were approximately 3.7% and 2.6% better than the state-of-the-art algorithms. Regarding performance with reference, we tested on the first Korean personal genome sequence data set, and our proposed method demonstrated a 189-fold compression rate, reducing the raw file size from 2986.8 MB to 15.8 MB at a comparable decompression cost with existing algorithms. DNAcompact is freely available at https://sourceforge.net/projects/dnacompact/for research purpose.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0080377PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3840021PMC
January 2015

An Adaptive Difference Distribution-based Coding with Hierarchical Tree Structure for DNA Sequence Compression.

Proc Data Compress Conf 2013;2013:371-380. Epub 2013 Mar 22.

Division of Biomedical Informatics University of California, San Diego San Diego, CA 92093, USA,

Previous reference-based compression on DNA sequences do not fully exploit the intrinsic statistics by merely concerning the approximate matches. In this paper, an adaptive difference distribution-based coding framework is proposed by the fragments of nucleotides with a hierarchical tree structure. To keep the distribution of difference sequence from the reference and target sequences concentrated, the sub-fragment size and matching offset for predicting are flexible to the stepped size structure. The matching with approximate repeats in reference will be imposed with the Hamming-like weighted distance measure function in a local region closed to the current fragment, such that the accuracy of matching and the overhead of describing matching offset can be balanced. A well-designed coding scheme will make compact both the difference sequence and the additional parameters, e.g. sub-fragment size and matching offset. Experimental results show that the proposed scheme achieves 150% compression improvement in comparison with the best reference-based compressor GReEn.
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http://dx.doi.org/10.1109/DCC.2013.45DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617277PMC
March 2013

SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL.

Proc Int Conf Image Proc 2012 :2157-2160

Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Object recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called "structure kernel", which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3648669PMC
http://dx.doi.org/10.1109/icip.2012.6467320DOI Listing
January 2012
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