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IEEE International Symposium on Biomedical Imaging

作者： Zhidong He Han Bao Mingzhang Chen Jiasong Wu Luwei Liu Lotfi Senhadji Huazhong Shu Bin Yan Laboratory of Image Science and Technology Southeast University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Ministry of Education) Southeast University Nanjing China Department of Orthodontics The Affiliated Stomatological Hospital of Nanjing Medical University Nanjing China Centre de Recherche en Information Biomédicale Sino-Français Rennes France INSERM LTSI-UMR 1099 Univ-Rennes Rennes France

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Landmark localization of facial soft tissue (FST) is a basic step in 3D morphometric analysis of human face. However, there are few studies on landmark localization of 3D scan images based on deep learning. The methods based on 2D images cannot be directly applied due to the non-Euclidean data structure. In this paper, we propose an end-to-end learning framework to automatically localize 28 landmarks on 3dMD scans, called FST-Net. Our method extracts features from texture images and mesh models. New texture mappings for 3dMD scans are generated by projection to fuse texture and structure features. A dual-branch network integrating transformers is applied to predict the landmark heatmaps from coarse to fine. A local coordinate regression module based on probabilistic distance and heatmap predictions is proposed to compute the landmark coordinates. We collect and annotate 297 3dMD face scans from the clinic to evaluate our model. Experiments show that the average localization error of the model is 1.204mm (the clinically acceptable precision range is 1.5 mm), and the correct landmark detection rate equals to 70.89%. Our model outperforms the current state-of-the-art deep learning methods for landmark localization on the mesh model.

关键词： Location awareness Deep learning Heating systems Three-dimensional displays Accuracy Computational modeling Biological tissues

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Weakly supervised person search with region siamese networks

arXiv

引用

arXiv 2021年

作者： Han, Chuchu Su, Kai Yu, Dongdong Yuan, Zehuan Gao, Changxin Sang, Nong Yang, Yi Wang, Changhu Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology ByteDance University of Technology Sydney

Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. Large-scale labeled training data is often difficult to collect, especially for person identities. A natural question is whether a good person search model can be trained without the need of identity supervision. In this paper, we present a weakly supervised setting where only bounding box annotations are available. Based on this new setting, we provide an effective baseline model termed Region Siamese Networks (R-SiamNets). Towards learning useful representations for recognition in the absence of identity labels, we supervise the R-SiamNet with instance-level consistency loss and cluster-level contrastive loss. For instance-level consistency learning, the R-SiamNet is constrained to extract consistent features from each person region with or without out-of-region context. For cluster-level contrastive learning, we enforce the aggregation of closest instances and the separation of dissimilar ones in feature space. Extensive experiments validate the utility of our weakly supervised method. Our model achieves the rank-1 of 87.1% and mAP of 86.0% on CUHK-SYSU benchmark, which surpasses several fully supervised methods, such as OIM [36] and MGTS [4], by a clear margin. More promising performance can be reached by incorporating extra training data. We hope this work could encourage the future research in this field. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

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Self-supervised learning for semi-supervised temporal action proposal

arXiv

引用

arXiv 2021年

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Shao, Yuanjie Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China DAMO Academy Alibaba Group China

Self-supervised learning presents a remarkable performance to utilize unlabeled data for various video tasks. In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action proposal generation. Particularly, we design an effective Self-supervised Semi-supervised Temporal Action Proposal (SSTAP) framework. The SSTAP contains two crucial branches, i.e., temporal-aware semi-supervised branch and relation-aware self-supervised branch. The semi-supervised branch improves the proposal model by introducing two temporal perturbations, i.e., temporal feature shift and temporal feature flip, in the mean teacher framework. The self-supervised branch defines two pretext tasks, including masked feature reconstruction and clip-order prediction, to learn the relation of temporal clues. By this means, SSTAP can better explore unlabeled videos, and improve the discriminative abilities of learned action features. We extensively evaluate the proposed SSTAP on THUMOS14 and ActivityNet v1.3 datasets. The experimental results demonstrate that SSTAP significantly outperforms state-of-the-art semi-supervised methods and even matches fully-supervised methods. Code is available at https://***/wangxiang1230/SSTAP. © 2021, CC BY.

关键词： Supervised learning

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Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation

arXiv

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arXiv 2024年

作者： Li, Jiyong Azizov, Dilshod Li, Yang Liang, Shangsong School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates AI Thrust Information Hub The Hong Kong University of Science and Technology Guangzhou China Department of CSE The Hong Kong University of Science and Technology China

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://***/lijy373/CCLIS. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Internal drivers of the global pandemic of the Omicron variants of SARS-CoV-2

Medicine Advances

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Medicine Advances 2024年第3期2卷 262-273页

作者： Yilin Niu Wuxiu Quan Yunyi Li Ziying Chen Yunfei Gao School of Public Health Southern Medical University Guangzhou Guangdong China Medical Big Data Center Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou Guangdong China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou Guangdong China School of Software Engineering South China University of Technology Guangzhou Guangdong China

Background The high transmissibility of the Omicron variants of severe acute respiratory syndrome coronavirus 2 continues to impose a significant burden on public health systems worldwide. Continuous mutations in the virus challenge the efficacy of vaccines and prior immunity from other variants, posing a severe threat to human health. Therefore, there is an urgent need to predict mutations of the Omicron variants and identify key factors influencing their spread. This study investigated critical amino acid mutations of the Omicron variants using epidemiological data and the mechanisms underlying their transmission. The aim was to understand the key factors driving the spread of the Omicron variants and provide insights for effective control and prevention strategies. Methods A total of 488,646 Omicron cases recorded between December 2021 and February 2023 were analyzed using a sliding time window and the Epi Score model, which has high accuracy for the identification of mutation sites. MutPred2, PolyPhen2 and VarSite tools were used to predict future mutations and identify factors driving the prevalence of the Omicron variants. Results Epi Scores showed fluctuating patterns at mutation sites, highlighting N969K, Y505H, N764K, T478K, and S371F mutations on the spike protein as significant for future prevention efforts. The spread of the Omicron variants was linked to changes in the viral entry pathway and improved angiotensin-converting enzyme 2 (ACE2) binding and immune evasion tactics. Conclusions The findings of this study reveal trends in the evolution of the Omicron variants, including altered cell entry, increased affinity for ACE2, and evasion of the immune system. These factors are critical for understanding the global spread of the Omicron variants and developing effective control strategies.

关键词： amino acids coronavirus immune evasion mutation public health SARS-CoV-2 spike glycoprotein

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Weakly Supervised Person Search with Region Siamese Networks

Weakly Supervised Person Search with Region Siamese Networks

引用

International Conference on Computer Vision (ICCV)

作者： Chuchu Han Kai Su Dongdong Yu Zehuan Yuan Changxin Gao Nong Sang Yi Yang Changhu Wang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology ByteDance University of Technology Sydney

ISBN: (纸本)9781665428132

关键词： Training Computer vision Annotations Supervised learning Training data Benchmark testing Feature extraction

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Closer to pre-trained network transfer better 9

Closer to pre-trained network transfer better

引用

9th IEEE Joint International Information Technology and Artificial intelligence Conference, ITAIC 2020

作者： Chen, Siyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Image Processing and Intelligent Control Key Laboratory Education Ministry of China Wuhan430074 China

In recent years, Deep Neural Network (DNN) has been widely used in the domain of computer vision, but its further development is restricted because of the lack of train samples. Fine-tuning is one of deep transfer learning methods to solve this problem. Nowdays, some existing Fine-tuning approaches assume that if the fine-tuned network could be as close to the pre-trained network as possible, the source knowledge would be fully utilized to improve the study effect. Although these approaches work pretty well, the assumptions on which they are based have not been systematically proved. Due to the complexity of DNNs, offering a reasonable and accurate explanation of the performance of the network is significant for designing new algorithms. In this paper, we design a series of experiments with or without transfer learning respectively, to characterize the relationship between network differences and network performance. The results show that there is an obvious negative correlation between them under certain circumstances. This conclusion can not only provide a more reasonable explanation for the relevant transfer learning algorithm, but also provide support and in-spiration for the future work. © 2020 IEEE.

关键词： Deep neural networks

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Multi-Centroid Representation Network for Domain Adaptive Person Re-ID

arXiv

引用

arXiv 2021年

作者： Wu, Yuhang Huang, Tengteng Yao, Haotian Zhang, Chi Shao, Yuanjie Han, Chuchu Gao, Changxin Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Megvii Technology China

Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the instance features from a cluster with the same pseudo label. However, a cluster may contain images with different identities (label noises) due to the imperfect clustering results, which makes the uni-centroid representation inappropriate. In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. Moreover, we further propose two strategies to improve the contrastive learning process. First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intra-domain information by comparing samples only from the same domain. Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant and informative negative samples. We integrate MCM, DSCL, and SONI into a unified framework named Multi-Centroid Representation Network (MCRN). Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Global burden of vision loss attributable to household air pollution from 1990 to 2019:A trend analysis and 10-year prediction

引用

Chinese Medical Journal 2024年第10期137卷 1231-1233页

作者： Cong Li Zuoqian Jing Jianbo Shen Shunming Liu Lei Liu Xiaohong Yang Honghua Yu Department of Ophthalmology Guangdong Eye InstituteGuangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong 510080China School of Medicine South China University of Technology Guangzhou 510006China Department of Ophthalmology The First Hospital of China Medical UniversityShenyangLiaoning 110001China Department of Neurosurgery Jincheng People’s HospitalJinchengShanxi 048026China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou 510080China

To the Editor:Blindness and vision loss(BVL)constitute a growing concern affecting an ever-expanding global population.[1]Globally,approximately 40%of the population relies on burning solid fuels for both cooking and *** air pollution(HAP)predominantly arises from the incomplete combustion of solid fuels used for ***,the adverse effects of HAP on eye health in human have ascended as a salient concern for environmental,ophthalmic,and public health.[2]Gaining a comprehensive understanding of the burden and temporal trends of BVL due to HAP is the essential basis for health program planning.

关键词： pollution cooking prediction

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DAM: Degradation-aware Model for Ultrasound image Quality Assessment

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Tuo Zhang, Xuan Ma, Xiuzhu Chen, Shuang Wang, Xuejuan Zhou, Ping Chen, Yang Zhou, Guangquan Lv, Faqin Southeast University School of Biological Science and Medical Engineering Nanjing 211189 China Chinese People's Liberation Army General Hospital Medical Center 3 Beijing 100039 China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing 210096 China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China Southeast University Jiangsu Key Laboratory of Biomaterials and Devices Nanjing 211189 China Southeast University State Key Laboratory of Digital Medical Engineering Nanjing 211189 China

One of the core challenges in ultrasound image quality assessment (IQA) is the entanglement of semantic content and quality-related information, such as blurring and shadows. Insufficient attention to the latter can easily lead to biased IQA results. Furthermore, fine-grained quality inconsistencies, i.e., subtle variations in ultrasound images that can impact quality interpretations, may further complicate the IQA tasks. To address these challenges, we propose a novel degradation-aware model (DAM) for the ultrasound IQA, which effectively perceives various and subtle variations of quality patterns, accurately assessing the quality of ultrasound images. The advanced degradation-derived augmentation (DDA) in DAM incorporates degradations that clinicians may focus on during IQA into the synthesis of appearance changes, promoting the disentanglement of quality-related representations from semantic contents. Subsequently, we present fine-grained degradation learning (FGDL), which encourages distinctions between image versions with diminishing quality inconsistencies, boosting the awareness of quality nuances from easy to hard for better ultrasound IQA performance. A universal boundary acquisition operator (UBAO) is also developed to suppress interferences from redundant information, achieving the standardization of ultrasound images from various devices. Extensive experimental results on an in-house ultrasound dataset demonstrate that DAM outperforms 14 baseline methods, achieving a PLCC of 0.760 and an SROCC of 0.766. © 2013 IEEE.

关键词： degradation-aware model degradation-derived augmentation fine-grained degradation learning Ultrasound image quality assessment universal boundary acquisition operator

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