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arXiv 2019年

作者： Zhu, Yabin Li, Chenglong Luo, Bin Tang, Jin Wang, Xiao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei Anhui China

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Multi-Adapter RGBT Tracking

Multi-Adapter RGBT Tracking

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Cheng Long Li Andong Lu Ai Hua Zheng Zhengzheng Tu Jin Tang Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms.

关键词： Adaptation models Robustness Target tracking Feature extraction Visualization Task analysis

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Joint object contour points and semantics for instance segmentation

arXiv

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arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Zhu, Mai Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science University of Auckland New Zealand

The attributes of object contours has great significance for instance segmentation task. However, most of the current popular deep neural networks do not pay much attention to the object edge information. Inspired by the human annotation process when making instance segmentation datasets, in this paper, we propose Mask Point R-CNN aiming at promoting the neural network’s attention to the object boundary. Specifically, we innovatively extend the original human keypoint detection task to the contour point detection of any object. Based on this analogy, we present an contour point detection auxiliary task to Mask R-CNN, which can boost the gradient flow between different tasks by effectively using feature fusion strategies and multi-task joint training. As a consequence, the model will be more sensitive to the edges of the object and can capture more geometric features. Quantitatively, the experimental results show that our approach outperforms vanilla Mask R-CNN by 3.8% on Cityscapes dataset and 0.8% on COCO dataset. Copyright © 2020, The Authors. All rights reserved.

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Equilibrium and transient thermodynamics: A unified dissipaton–space approach

arXiv

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arXiv 2020年

作者： Gong, Hong Wang, Yao Zhang, Hou-Dao Qiao, Qin Xu, Rui-Xue Zheng, Xiao Yan, YiJing Department of Chemical Physics University of Science and Technology of China HefeiAnhui230026 China Hefei National Laboratory for Physical Sciences at the Microscale and iChEM Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Digital Medical Research Center of School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

This work presents a unified dissipaton–equation–of–motion (DEOM) theory and its evaluations on the Helmholtz free energy change due to the isotherm mixing of two isolated subsystems. One is a local impurity and another is a nonlocal Gaussian bath. DEOM constitutes a fundamental theory for such open quantum mixtures. To complete the theory, we construct also the imaginary–time DEOM formalism via an analytical continuation of dissipaton algebra, which would be limited to equilibrium thermodynamics. On the other hand, the real–time DEOM deals with both equilibrium structural and nonequilibrium dynamic properties. Its combination with the thermodynamic integral formalism would be a viable and accurate means to both equilibrium and transient thermodynamics. As illustrations, we report the numerical results on a spin–boson system, with elaborations on the underlying anharmonic features, the thermodynamic entropy versus the von Neumann entropy, and an indication of "solvent–cage" formation. Beside the required asymptotic equilibrium properties, the proposed transient thermodynamics also supports the basic spontaneity criterion. Copyright © 2020, The Authors. All rights reserved.

关键词： Free energy

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A novel cross-modal hashing algorithm based on multimodal deep learning

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Science China(Information Sciences) 2017年第9期60卷 50-63页

作者： Wen QU Daling WANG Shi FENG Yifei ZHANG Ge YU School of Information Science and Engineering Northeastern University Key Laboratory of Medical Image Computing

With the growing popularity of multimodal data on the Web, cross-modal retrieval on large-scale multimedia databases has become an important research topic. Cross-modal retrieval methods based on hashing assume that there is a latent space shared by multimodal features. To model the relationship among heterogeneous data, most existing methods embed the data into a joint abstraction space by linear projections. However,these approaches are sensitive to noise in the data and are unable to make use of unlabeled data and multimodal data with missing values in real-world applications. To address these challenges, we proposed a novel multimodal deep-learning-based hash(MDLH) algorithm. In particular, MDLH uses a deep neural network to encode heterogeneous features into a compact common representation and learns the hash functions based on the common representation. The parameters of the whole model are fine-tuned in a supervised training *** on two standard datasets show that the method achieves more effective results than other methods in cross-modal retrieval.

关键词： hashing cross-modal retrieval cross-modal hashing multimodal data analysis deep learning

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Visualizing the neuroanatomical changes in Han Chinese adulthood: A pseudo-longitudinal study based on age-related large-scale statistical Chinese brain atlases

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Brain Science Advances 2020年第2期5卷 106 - 116页

作者： Lin Shi Peipeng Liang Andy Li Raymond Wong Yishan Luo Kai Liu Lening Li Kuncheng Li Research Center for Medical Image Computing The Chinese University of Hong Kong Hong Kong 999077 ChinaDepartment of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong 999077 China School of Psychology Beijing Key Laboratory of Learning and Cognition Capital Normal University Beijing 10048 China BrainNow Research Institute Shenzhen 518081 Guangdong Province China Shenzhen SmartView MedTech Limited Shenzhen 518081 Guangdong Province China Department of Radiology Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Xuanwu Hospital Capital Medical University Beijing 100053 China

Objective: Understanding how brain changes over lifetime provides the basis for new insights into neurophysiology and neuropathology. In this study, we carried out a pseudo-longitudinal study based on age-related Chinese brain atlases (i.e., Chinese2020) constructed from large-scale volumetric brain MRI data collected in normal Han Chinese adults at varying ages. Methods: In order to quantify the deformation and displacement of brains for each voxel as age increases, optical flow algorithm was employed to compute motion vectors between every two consecutive brain templates of the age-related brain atlas, i.e., Chinese2020. Results: Dynamic age-related neuroanatomical changes in a standardized brain space were shown. Overall, our results demonstrate that brain inward deformation (mainly due to atrophy) can appear in adulthood and this trend generally accelerates as age increases, affecting multiple regions including frontal cortex, temporal cortex, parietal cortex, and cerebellum, whereas occipital cortex is least affected by aging, and even showed some degree of outward deformation in the midlife. Conclusion: Our findings indicated more complicated age-related changes instead of a simple trend of brain volume decrease, which may be in line with the recently increasing interests in the age-related cortical complexity with other morphometry measures.

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Common and Rare Fundus Diseases Identification Using Vision-Language Foundation Model with Knowledge of Over 400 Diseases

arXiv

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arXiv 2024年

作者： Wang, Meng Lin, Tian Lin, Aidi Yu, Kai Peng, Yuanyuan Wang, Lianyu Chen, Cheng Zou, Ke Liang, Huiyu Chen, Man Yao, Xue Zhang, Meiqin Huang, Binwei Zheng, Chaoxin Zhang, Peixin Chen, Wei Luo, Yilong Chen, Yifan Xia, Honghe Shi, Tingkun Zhang, Qi Guo, Jinming Chen, Xiaolin Wang, Jingcheng Chung Tham, Yih Liu, Dianbo Wong, Wendy Thakur, Sahil Fenner, Beau Fang, Danqi Liu, Siying Liu, Qingyun Huang, Yuqiang Zeng, Hongqiang Meng, Yanda Zhou, Yukun Jiang, Zehua Qiu, Minghui Zhang, Changqing Chen, Xinjian Wang, Sophia Y. Lee, Cecilia S. Sobrin, Lucia Cheung, Carol Y. Pang, Chi Pui Keane, Pearse A. Cheng, Ching-Yu Chen, Haoyu Fu, Huazhu Centre for Innovation & Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore117549 Singapore Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore117549 Singapore Joint Shantou International Eye Center Shantou University Chinese University of Hong Kong Guangdong Shantou515041 China Department of Radiology University of Pennsylvania PhiladelphiaPA19104 United States School of Biomedical Engineering Anhui Medical University Anhui Hefei230032 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211100 China Center of Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA02114 United States National Key Laboratory of Fundamental Science on Synthetic Vision College of Computer Science Sichuan University Sichuan Chengdu610065 China Big Vision Medical Technology Ltd. Suzhou China Singapore Eye Research Institute Singapore National Eye Centre Singapore Duke-NUS Medical School Singapore Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong 999077 Hong Kong Shenzhen Longgang E.N.T Hospital Guangdong Shenzhen518172 China Dongguan Songshan Lake Central Hospital Guangdong Dongguan523326 China Department of Computer Science University of Exeter ExeterEX4 4RN United Kingdom Centre for Medical Image Computing University College London London United Kingdom NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Institute of Ophthalmology University College London London United Kingdom Tsinghua Medicine of Tsinghua University Beijing100084 China School of Clinical Medicine Beijing Tsinghua Changgung Hospital Beijing102218 China Foshan Aier Eye Hospital Guangdong Foshan528000 China College of Intelligence and Computing Tianjin University Tianjin300350 China School of Electr

Previous foundation models for retinal images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language foundation model that leverages knowledge from over 400 fundus diseases. For RetiZero's pre-training, we compiled 341,896 fundus images paired with text descriptions, sourced from public datasets, ophthalmic literature, and online resources, encompassing a diverse range of diseases across multiple ethnicities and countries. RetiZero exhibits superior performance in several downstream tasks, including zero-shot disease recognition, image-to-image retrieval, and internal- and cross-domain disease identification. In zero-shot scenarios, RetiZero achieves Top5 accuracy scores of 0.8430 for 15 fundus diseases and 0.7561 for 52 fundus diseases. For image retrieval, it achieves Top5 scores of 0.9500 and 0.8860 for the same disease sets, respectively. Clinical evaluations show that RetiZero's Top3 zero-shot performance surpasses the average of 19 ophthalmologists from Singapore, China, and the United States. Furthermore, RetiZero significantly enhances clinicians' accuracy in diagnosing fundus disease. These findings underscore the value of integrating the RetiZero foundation model into clinical settings, where a variety of fundus diseases are encountered. © 2024, CC0.

关键词： Ophthalmology

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A Review of intelligent image Processing Method of Pulmonary CT images

A Review of Intelligent Image Processing Method of Pulmonary...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenjun Tan Pan Liu Hui Guo Jinzhu Yang Xiaoxia Yin Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Centre for Applied Informatics College of Engineering and Science Victoria University Melbourne Australia

ISBN: (纸本)9781728118680

Due to the high degree of air pollution, high smoking rate and poor production protection conditions during the industrialization process, the incidence of pulmonary diseases such as pulmonary cancer and chronic obstructive pulmonary disease has been maintained at a high level in the world and has shown an increasing trend, which has become one of the diseases with the highest mortality rate. In recent years, with the rapid development of artificial intelligence technology, the intelligent assisted diagnosis and surgery technology of pulmonary CT images has made great progress, and played an important role in the early diagnosis and precise treatment of pulmonary diseases. This paper mainly discusses the development history of image-assisted diagnosis and surgery technology for pulmonary diseases, as well as the method of segmentation and evaluation of pulmonary tissue and lesion, and the clinical application of pulmonary intelligent assisted diagnosis and surgery systems.

关键词： biological tissues cancer computerised tomography image segmentation lung medical image processing surgery X-Ray Computed Tomography medical image processing Lung TISSUES (BIOLOGY) Lung Diseases image segmentation Surgery Air contamination intelligent image processing Lung Cancer Chronic Obstructive Airway Disease Mortality

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HMOE-Net: Hybrid Multi-scale Object Equalization Network for Intracerebral Hemorrhage Segmentation in CT images

HMOE-Net: Hybrid Multi-scale Object Equalization Network for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xizhi He Kai Chen Kai Hu Zhineng Chen Xuanya Li Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Affiliated Hospital of Xiangnan University Chenzhou China Institute of Automation Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China College of Medical Imaging and Inspection Xiangnan University Chenzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

In this paper, we propose a novel Hybrid Multi-scale Object Equalization Network (HMOE-Net) to segment intracerebral hemorrhage (ICH) regions. In particular, we design a shallow feature extraction network (SFENet) and a deep feature extraction network (DFENet) to solve the problem of equalization learning of hybrid multi-scale object features. The multi-level feature extraction (MLFE) blocks are presented in DFENet to explore multi-level semantic features more effectively. Furthermore, we adopt a progressive feature extraction strategy combining SFENet and DFENet to further consider the differences of various ICH regions and achieve the equalization feature learning of multi-scale objects. To verify the effectiveness of HMOE-Net, we collect a clinical ICH dataset with a total of 500 CT cases from three hospitals for the evaluation. The experimental results show that HMOE-Net is superior to six state-of-the-art methods and achieves accurate segmentation for multi-scale ICH regions.

关键词： Feature extraction Semantics image segmentation Data mining Hemorrhaging Computed tomography Convolution

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An Adaptive Clustering Algorithm of Gaussian Curve Fitting the Histogram Based on K-Means

An Adaptive Clustering Algorithm of Gaussian Curve Fitting t...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Guangkun Ma Jinzhu Yang Xiaoxu Song Hong Zhao School of Computer Science and Engineering Northeastern University Shenyang China School of Software Shenyang University of Technology Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781728152103

The K-mean algorithm is simple and efficient in vascular segmentation. But in a K-means clustering algorithm, the number of clustering centers must be determined in advance and the random allocation of clustering centers seriously affects the efficiency of the algorithm. In order to solve this problem, an adaptive clustering algorithm based on K-means with the Gaussian curve fitting histogram is proposed, which is used to segment circular-like blood vessels. Firstly, the histogram outline is fitted into the Gaussian curve by the least square method and the maximum value of the curve is obtained. The number of maximal values is counted as the number of clustering centers and the maximum points are used as the central points of clustering. Also, the K-means algorithm is improved by combining the intensity and position information of pixels. The proposed method has been tested by 6 groups of medical images. The algorithm has the advantages of less iterations, fast convergence speed and getting more than 95% Dice Similarity Measure value. The algorithm can effectively solve the determination of multi-seed points and vascular segmentation in medical image segmentation.

关键词： blood vessels curve fitting Gaussian processes image segmentation medical image processing pattern clustering Gaussian distribution image segmentation Clustering algorithms medical image processing Cluster Analysis Gaussian processes Curve fitting Blood Vessels histograms Least squares method

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