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SSRN 2023年

作者： Guo, Tianjiao Yang, Jie Yu, Qi Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong Univeristy Shanghai China Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Clinical Research Center for Eye Diseases Shanghai China

The segmentation of diabetic retinopathy (DR) lesions is important for large-scale screening using color fundus photography (CFP) images. The difficulty of this task is that the DR lesions have various sizes, shapes, and intensities. Traditional handcrafted feature-based approaches are unsatisfactory, and recent deep-learning-based approaches ignore the features of DR lesions. In this paper, we propose a segmentation approach that segments four typical DR lesions simultaneously based on convolutional neural networks (CNN). The raw CFP image is first pre-processed and resized to different sizes. A set of fully convolutional neural networks (FCNs) with various input sizes are then trained to extract the lesions from different scales. An auxiliary CNN is then introduced to fuse the output from these FCNs and refine the segmentation *** conducted our experiments on one local dataset and three public datasets including IDRiD, DDR, and DIARETDB1. The results of the area under the precision-recall-curve (AUPR) show that our approach achieves competitive *** improvement in performance indicates that this approach is beneficial to DR lesion segmentation and has potential in other segmentation tasks. © 2023, The Authors. All rights reserved.

关键词： Convolution

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One-shot distributed algorithm for PCA with RBF kernels

arXiv

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arXiv 2020年

作者： He, Fan Lv, Kexin Yang, Jie Huang, Xiaolin The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China The MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China

This letter proposes a one-shot algorithm for feature-distributed kernel PCA. Our algorithm is inspired by the dual relationship between sample-distributed and feature-distributed scenario. This interesting relationship makes it possible to establish distributed kernel PCA for feature-distributed cases from ideas in distributed PCA in sample-distributed scenario. In theoretical part, we analyze the approximation error for both linear and RBF kernels. The result suggests that when eigenvalues decay fast, the proposed algorithm gives high quality results with low communication cost. This result is also verified by numerical experiments, showing the effectiveness of our algorithm in practice. Copyright © 2020, The Authors. All rights reserved.

关键词： Principal component analysis

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Pulsar candidate selection using ensemble networks for FAST drift-scan survey

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Science China(Physics,Mechanics & Astronomy) 2019年第5期62卷 61-70页

作者： HongFeng Wang WeiWei Zhu Ping Guo Di Li SiBo Feng Qian Yin ChenChen Miao ZhenZhao Tao ZhiChen Pan Pei Wang Xin Zheng XiaoDan Deng ZhiJie Liu XiaoYao Xie XuHong Yu ShanPing You Hui Zhang FAST Collaboration Image Processing and Pattern Recognition Laboratory College of Information Science and TechnologyBeijing Normal UniversityBeijing 100875China CAS Key Laboratory of FAST Chinese Academy of SciencesBeijing 100101China School of Information Management Dezhou UniversityDezhou 253023China Image Processing and Pattern Recognition Laboratory School of Systems ScienceBeijing Normal UniversityBeijing 100875China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China 不详

The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope(FAST) Survey(CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system(PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks(CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify >1.6 million candidates per day using a dual-GPU and 24-core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches.

关键词： pulsars neural networks data analysis

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Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection

Dynamically Mitigating Data Discrepancy with Balanced Focal ...

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International Conference on pattern recognition

作者： Yongqiang Dou Haocheng Yang Maolin Yang Yanyan Xu Dengfeng Ke Engineering Research Center for Forestry-oriented Intelligent Information Processing National Forestry and Grassland Administration National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. Therefore, to mitigate the data discrepancy between training and inference, we propose to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go.

关键词： Training Learning systems Deep learning Bridges Time-frequency analysis Neural networks Detectors

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Indoor Elderly Fall recognition System Based on Target Detection and Self-attention Model

Indoor Elderly Fall Recognition System Based on Target Detec...

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International Conference on Electronics, Communications and Information Technology (CECIT)

作者： Min Liu Xufeng Ling Jie Yang School of Health Tianhua College Shanghai Normal University Shanghai China AI School Tianhua College Shanghai Normal University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781665437585

With the increasing aging of the society, a series of consequences caused by falls of the elderly have become a serious medical problem and a real social problem. Aiming at the fall behavior of the elderly, the research and development of human fall recognition technology has practical application value. This paper proposes a method to recognize human targets in indoor images using target detection technology, and then use the trained neural network based on Self-attention technique named Vision Transformer (ViT) to recognize the falling posture of the elderly. Firstly, the target detection model is optimized and trained for indoor scene, so that the model can accurately detect human targets in indoor scene; Next, the indoor scene human target image is preprocessed, and the indoor human pose image data set is constructed; Then, the data set is used to train the ViT depth network, and enable it to correctly classify five postures (standing, sitting, lying, bending and crawling). The experimental results show that ViT network achieves high accuracy and excellent generalization capacity for the above 5 human posture and can realize the recognition of elderly falls.

关键词： Technological innovation image recognition Target recognition Object detection Transformers Data models Robustness

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Stereo endoscopic image super-resolution using disparity-constrained parallel attention

arXiv

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arXiv 2020年

作者： Zhang, Tianyi Gu, Yun Huang, Xiaolin Tu, Enmei Yang, Jie Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

With the popularity of stereo cameras in computer assisted surgery techniques, a second viewpoint would provide additional information in surgery. However, how to effectively access and use stereo information for the super-resolution (SR) purpose is often a challenge. In this paper, we propose a disparity-constrained stereo super-resolution network (DCSSRnet) to simultaneously compute a super-resolved image in a stereo image pair. In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules. Experiment results on laparoscopic images demonstrate that the proposed framework outperforms current SR methods on both quantitative and qualitative evaluations. Our DCSSRnet provides a promising solution on enhancing spatial resolution of stereo image pairs, which will be extremely beneficial for the endoscopic surgery. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Colonoscopic image Synthesis For Polyp Detector Enhancement Via Gan And Adversarial Training

Colonoscopic Image Synthesis For Polyp Detector Enhancement ...

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IEEE International Symposium on Biomedical Imaging

作者： Fan He Sizhe Chen Shuaiyi Li Lu Zhou Haiqin Zhang Haixia Peng Xiaolin Huang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Digestive Endoscopy Center Tongren Hospital Shanghai Jiao Tong University School of Medicine Institute of Medical Robotics Shanghai Jiao Tong University

Computer-aided polyp detection system powered by deep neural networks has achieved high performance but also suffers from data insufficiency. To address this problem, recent researches focus on synthesizing new colonoscopic images by Generative Adversarial Network (GAN). However, the synthesized images follow the same distribution as that of the training dataset, which limits the performance of the detectors re-trained on it. Recent studies show that adversarial examples can expand the data distribution and thus adversarial training can effectively improve the robustness of deep neural networks. Inspired by these two factors, this paper proposes a data augmentation framework to directly produce false negative colonoscopic images via GAN and the adversarial attack. The synthesized polyps are natural and experiments on three popular detectors show that compared with using GAN alone, producing false negative images by the adversarial attack can further improve the performance of the re-trained detectors.

关键词： Training image synthesis Neural networks Deep architecture Detectors Generative adversarial networks Robustness

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Corrigendum to “FGPNet: A weakly supervised fine-grained 3D point clouds classification network” [pattern recognition 139 (2023) 109509]

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pattern recognition 2024年 151卷

作者： Huihui Shao Jing Bai Rusong Wu Jinzhe Jiang Hongbo Liang the School of Computer Science and Engineering North Minzu University Yinchuan 750021 China the Key Laboratory of Images Processing and Pattern Recognition Laboratory Commission: IPPRLab North Minzu University Yinchuan 750021 China

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An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume 23rd

An Enhanced Coarse-to-Fine Framework for the Segmentation of...

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Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Qian, Dahong Liu, Weiping Li, Hui Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Algorithm and Research Shanghai Aitrox Technology Co. Ltd. Shanghai China National Key Laboratory of Science and Technology on Nano/Micro Fabrication Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Institute of Micro-Nano Science and Technology Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783030718268

In radiation therapy, obtaining accurate boundary of the clinical target volume (CTV) is the vital step to decrease the risk of treatment failures. However, it is a time-consuming and laborious task to obtain the delineation by hand. Therefore, an automatic algorithm is urgently needed to realize accurate segmentation. In this paper, we propose an enhanced coarse-to-fine frameworkto automatically fuse the information of CT, T1 and T2 images to get the target region. This framework includes a coarse-segmentation stage to identify the region of interest (ROI) of targets and a fine-segmentation stage to iteratively refine the segmentation. In the coarse-segmentation stage, the F-loss is proposed to keep the high recall rate of the ROI. In the fine segmentation, the ROI of target will be first cropped according to the ROI obtained by coarse-segmentation and be fed into a 3D-Unet to get the initial results. Then, the prediction and medium features will be set as additional information for the next one network to refine the results. When evaluated on the validation dataset of challenge of Anatomical Brain Barriers to Cancer Spread (ABCs), our method won the 3th place in the public leaderboard. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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A novel TSK fuzzy system incorporating multiview collaborative transfer learning for personalized epileptic EEG detection

arXiv

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arXiv 2021年

作者： Li, Andong Deng, Zhaohong Lou, Qiongdan Choi, Kup-Sze Shen, Hongbin Wang, Shitong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Centre for Smart Health Hong Kong Polytechnic University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset. © 2021, CC BY-NC-ND.

关键词： Electrophysiology

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