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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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Efficient image Super-Resolution with Feature Interaction Weighted Hybrid Network

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Yang, Jian Qi, Guo-Jun Lin, Chia-Wen Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100080 China Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China Research Center for Industries of the Future the School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan

Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss of middle-layer features due to activation functions. To minimize the impact of intermediate feature loss on reconstruction quality, we propose a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone. Every third WDIB forms a Feature Shuffle Weighted Group (FSWG) by applying mutual information shuffle and fusion. Moreover, to mitigate the negative effects of intermediate feature loss, we introduce Wide Residual Weighting units within WDIB. These units effectively fuse features of varying levels of detail through a Wide-residual Distillation Connection (WRDC) and a Self-Calibrating Fusion (SCF). To compensate for global feature deficiencies, we incorporate a Transformer and explore a novel architecture to combine CNN and Transformer. We show that our FIWHN achieves a favorable balance between performance and efficiency through extensive experiments on low-level and high-level tasks. Codes will be available at https://***/IVIPLab/FIWHN. Copyright © 2022, The Authors. All rights reserved.

关键词： image reconstruction

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Attention Combination of Sequence Models for Handwritten Chinese Text recognition

Attention Combination of Sequence Models for Handwritten Chi...

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International Workshop on Frontiers in Handwriting recognition

作者： Zheng-Yu Zhu Fei Yin Da-Han Wang Colllege of information science and engineering China University of Petroleum Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China College of computer and information engineering Xiamen University of Technology Xiamen China

ISBN: (数字)9781728199665

ISBN: (纸本)9781728199672

Handwritten Chinese text recognition (HCTR) methods can generally be divided into two categories: explicit segmentation based methods and implicit segmentation based methods. The explicit segmentation based approaches are superior in the accuracy rate of character recognition, while the implicit segmentation based approaches can provide more fluent recognition result due to the end-to-end recognition mode of it. In this paper, we propose to combine this two kinds of approaches using convolutional combination strategy. The proposed system includes multiple encoders and a decoder in the form of full convolution. It combines the recognition results of explicit segmentation based HCTR methods and implicit segmentation based HCTR methods, and generates the final recognition output. A novel attention combination mechanism is proposed to solve this one-to-many attention problem. Experiments on the ICDAR13 dataset show that the proposed method improves the accurate rate by 4.96%. The experimental results demonstrate the effectiveness of proposed method.

关键词： Character recognition image segmentation Convolution Text recognition Decoding Deep learning Hidden Markov models

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A Method for Authenticity Identification of Fritillaria Cirrhosa D. Don Based on Deep Learning 4

A Method for Authenticity Identification of Fritillaria Cirr...

引用

4th IEEE International Conference on image, Vision and Computing, ICIVC 2019

作者： Hu, Ke Hu, Pan Cao, Dong Yan, Xin Yu, Xi Liu, Chang Chengdu University College of Information Science and Engineering Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu China Research Institute of Big Data Chengdu University China Chengdu Institute of Chinese Herbal Medicine China

ISBN: (纸本)9781728123257

Since the authentic Fritillaria Cirrhosa D. Don resources are scarce due to its high price and valuable medical uses, it is difficult to meet the clinical needs. Therefore, the problem of adulteration in the market is more and more serious. At present, the identification of Fritillaria mainly relies on traditional trait identification, microscopic identification, physical and chemical identification and other methods, which is subjective and requires high practical experience for operators, and the pretreatment work is cumbersome. In this paper, the concept of deep learning is introduced into the identification of Fritillaria for the first time. First, we have created the first multi-angle Fritillaria standard datasets, including Fritillaria Cirrhosa D. Don and Fritillary bulb. We then have proposed a novel deep learning framework SE-DPU to classify Fritillaria datasets, from which we can get the highest classification accuracy. The framework is a hybrid of Squeeze-and-Excitation block (SE unit), U-Net and Dual Path Network (DPN). It reuses the features and explores the new features adaptively recalibrates channel-wise feature responses obtains abundance features while training. Experimental results with Fritillaria Cirrhosa D. Don data have indicated that the proposed method have provided competitive performance. © 2019 IEEE.

关键词： Deep learning

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Neuron segmentation using 3D wavelet integrated encoder-decoder network

arXiv

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arXiv 2021年

作者： Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen518060 China

Motivation: 3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task. Results: In this paper, we propose a 3D wavelet and deep learning based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes;the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction. © 2021, CC BY.

关键词： Neurons

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A comprehensive survey of oracle character recognition: Challenges, datasets, methodology, and beyond

引用

pattern recognition 2026年 169卷

作者： Jing Li Xueke Chi Qiufeng Wang Kaizhu Huang Da-Han Wang Yongge Liu Cheng-Lin Liu College of Science & Technology Ningbo University Ningbo China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Duke Kunshan University Suzhou China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen China Anyang Normal University Anyang China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Oracle character recognition — an analysis of ancient Chinese inscriptions found on oracle bones — has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement toward the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.

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Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

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Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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EEG-Based Brain-computer Interfaces Are Vulnerable to Backdoor Attacks

Research Square

引用

Research Square 2021年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs. © 2021, CC BY.

关键词： Electroencephalography

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