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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhu, Yiheng Wu, Jialu Li, Qiuyi Yan, Jiahuan Yin, Mingze Wu, Wei Li, Mingyang Ye, Jieping Wang, Zheng Wu, Jian College of Computer Science & Technology Liangzhu Laboratory Zhejiang University China College of Pharmaceutical Sciences Zhejiang University China Alibaba Cloud Computing China School of Public Health Zhejiang University China School of Artificial Intelligence and Data Science University of Science and Technology of China China The Second Affiliated Hospital Zhejiang University School of Medicine China

Inverse protein folding is a fundamental task in computational protein design, which aims to design protein sequences that fold into the desired backbone structures. While the development of machine learning algorithms for this task has seen significant success, the prevailing approaches, which predominantly employ a discriminative formulation, frequently encounter the error accumulation issue and often fail to capture the extensive variety of plausible sequences. To fill these gaps, we propose Bridge-IF, a generative diffusion bridge model for inverse folding, which is designed to learn the probabilistic dependency between the distributions of backbone structures and protein sequences. Specifically, we harness an expressive structure encoder to propose a discrete, informative prior derived from structures, and establish a Markov bridge to connect this prior with native sequences. During the inference stage, Bridge-IF progressively refines the prior sequence, culminating in a more plausible design. Moreover, we introduce a reparameterization perspective on Markov bridge models, from which we derive a simplified loss function that facilitates more effective training. We also modulate protein language models (PLMs) with structural conditions to precisely approximate the Markov bridge process, thereby significantly enhancing generation performance while maintaining parameter-efficient training. Extensive experiments on well-established benchmarks demonstrate that Bridge-IF predominantly surpasses existing baselines in sequence recovery and excels in the design of plausible proteins with high foldability. The code is available at https://***/violet-sto/Bridge-IF. © 2024 Neural information processing systems foundation. All rights reserved.

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Mixed Prototype Correction for Causal Inference in Medical Image Classification 24

Mixed Prototype Correction for Causal Inference in Medical I...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Yajie Huang, Zhi-An Hong, Zhiliang Wu, Songsong Wu, Jibin Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong Dongguan China Shengli Clinical Medical College Fujian Medical University Fujian China Ultrasound Department Fuzhou University Affiliated Provincial Hospital Fuzhou China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798400706868

The heterogeneity of medical images poses significant challenges to accurate disease diagnosis. To tackle this issue, the impact of such heterogeneity on the causal relationship between image features and diagnostic labels should be incorporated into model design, which however remains underexplored. In this paper, we propose a mixed prototype correction for causal inference (MPCCI) method, aimed at mitigating the impact of unseen confounding factors on the causal relationships between medical images and disease labels, so as to enhance the diagnostic accuracy of deep learning models. The MPCCI comprises a causal inference component based on front-door adjustment and an adaptive training strategy. The causal inference component employs a multi-view feature extraction (MVFE) module to establish mediators, and a mixed prototype correction (MPC) module to execute causal interventions. Moreover, the adaptive training strategy incorporates both information purity and maturity metrics to maintain stable model training. Experimental evaluations on four medical image datasets, encompassing CT and ultrasound modalities, demonstrate the superior diagnostic accuracy and reliability of the proposed MPCCI. The code will be available at https://***/Yajie-Zhang/MPCCI. © 2024 Owner/Author.

关键词： Deep learning

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Augmented Reality-Assisted Pediatric Surgery: A Machine Learning-Based Approach for Enhancing Accuracy 1

Augmented Reality-Assisted Pediatric Surgery: A Machine Lear...

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1st International Conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Bhardwaj, Sumedha Narla, Swapna Absur, Md Nurul Dutta, Shuvo Hyzil, J.B. Krishna Ganesh, S.M. DIT University Department of School of Computing Uttarakhand Dehradun India Tek Yantra Inc CA United States CUNY Graduate Center New YorkNY United States Western Michigan University Department of Physics United States Govt College for Women Affiliated to University of Kerala Department of Zoology Thiruvananthapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331518578

Numerous advancements have been identified as potentially disruptive innovations in the surgical workplace as surgical workflow continues to improve in the digital age. The application of virtual reality (VR) and augmented reality (AR) within health care to enhance the use of data for medical reasons is unavoidable given their rapid expansion in availability, affordability, and accessibility. Using augmented reality may help paediatric orthopaedic and trauma surgeons overcome the difficult problem of comprehending the spatial linkages among the implants, anatomy, and their instruments. As needs be, the current examination proposes another AR of the surgical scene that deftly incorporates the various information sources given by a mobile C-arm and a Kinect RGB-depth sensor. This paper present a learning-based worldview to perceive the significant items or life structures in both Kinect and data of X-ray, and to produce an object unambiguous pixel-wise alpha guide that permits importance based combination of the X-ray pictures and the video inside a solitary view. We demonstrate very encouraging results in 10 simulated procedures with the goal of giving physicians a better grasp of the surgical environment and an enhanced perception. © 2025 IEEE.

关键词： Pediatrics

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An Early Detection Model of Prostate Cancer Based on MRI 9

An Early Detection Model of Prostate Cancer Based on MRI

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Liu, Yuankun Xing, Lumin Wang, Di Nie, Xin Zhang, Xueyan Wu, Di Liu, Wenjian The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Chongqing Jianzhu College Chongqing China Shandong Youth University of Political Science School of Information Work Office Jinan China University of Southern Queensland School of Mathematics Physics and Computing QLD Australia

ISBN: (纸本)9798350391367

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively. © 2024 IEEE.

关键词： Urology

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Bridge-IF: learning inverse protein folding with Markov bridges 24

Bridge-IF: learning inverse protein folding with Markov brid...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yiheng Zhu Jialu Wu Qiuyi Li Jiahuan Yan Mingze Yin Wei Wu Mingyang Li Jieping Ye Zheng Wang Jian Wu College of Computer Science & Technology and Liangzhu Laboratory Zhejiang University College of Pharmaceutical Sciences Zhejiang University Alibaba Cloud Computing School of Public Health Zhejiang University School of Artificial Intelligence and Data Science University of Science and Technology of China College of Computer Science & Technology and Liangzhu Laboratory Zhejiang University and School of Public Health Zhejiang University and The Second Affiliated Hospital Zhejiang University School of Medicine

ISBN: (纸本)9798331314385

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Bridge-IF: Learning Inverse Protein Folding with Markov Bridges

arXiv

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arXiv 2024年

作者： Zhu, Yiheng Wu, Jialu Li, Qiuyi Yan, Jiahuan Yin, Mingze Wu, Wei Li, Mingyang Ye, Jieping Wang, Zheng Wu, Jian College of Computer Science & Technology and Liangzhu Laboratory Zhejiang University China College of Pharmaceutical Sciences Zhejiang University China Alibaba Cloud Computing China School of Public Health Zhejiang University China School of Artificial Intelligence and Data Science University of Science and Technology of China China The Second Affiliated Hospital Zhejiang University School of Medicine China

关键词： Markov processes

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Evaluating the Use of Large Language Model to Identify Top Research Priorities in Hematology

Evaluating the Use of Large Language Model to Identify Top R...

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2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023

作者： Chen, Huiqin Huang, Fangwan Liu, Xuanyun Tan, Yinli Zhang, Dihua Li, Dongqi Borchert, Glen M. Tan, Ming Huang, Jingshan Sun Yatsen University Department of Pediatrics the Third Affiliated Hospital Guangzhou China College of Computer and Data Science Fuzhou University Fuzhou China Sun Yat-sen University Department of Nephrology the First Affiliated Hospital Guangzhou China University of California Irvine Department of Economics and School of Information and Computer Sciences Irvine United States College of Medicine University of South Alabama Department of Pharmacology Mobile United States Institute of Biochemistry and Molecular Biology Institute of Biomedical Sciences China Medical University Taichung Taiwan School of Computing College of Medicine University of South Alabama Mobile United States

ISBN: (纸本)9798350302301

The field of hematology is highly progressive and dynamic requiring researchers to commit significant amounts of time and effort towards staying abreast of the most crucial research areas. As such, in this work we assess the potential of ChatGPT for identifying research priorities within five key topics in hematology: acute lymphocytic leukemia, immunotherapy, targeted therapy, hematopoietic stem cell transplantation, and acute myeloid leukemia. After ChatGPT was employed to generate specific research questions in these areas, a panel of seven experienced hematologists independently reviewed and rated resultant research questions based on four parameters: relevance, originality, clarity, and specificity on a scale of 1 to 5, with 5 denoting the highest score. Excitingly, the mean and median grades of the four parameters were all above 4, indicating that the hematologists strongly agreed that the problems generated by ChatGPT were generally highly specific, clear, relevant, and original. As such, although further work will clearly be required, we suggest our current study indicates that Large Language Models (LLMs), such as ChatGPT, may very well represent valuable new tools for more efficiently identifying and prioritizing impactful research questions in the field of hematology. © 2023 IEEE.

关键词： Stem cells

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ProtCLIP: Function-Informed Protein Multi-Modal Learning

arXiv

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arXiv 2024年

作者： Zhou, Hanjing Yin, Mingze Wu, Wei Li, Mingyang Fu, Kun Chen, Jintai Wu, Jian Wang, Zheng College of Computer Science and Technology Zhejiang University China State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital School of Medicine Zhejiang University China Alibaba Cloud Computing School of Artificial Intelligence and Data Science University of Science and Technology of China China Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence China

Multi-modality pre-training paradigm that aligns protein sequences and biological descriptions has learned general protein representations and achieved promising performance in various downstream applications. However, these works were still unable to replicate the extraordinary success of language-supervised visual foundation models due to the ineffective usage of aligned protein-text paired data and the lack of an effective function-informed pre-training paradigm. To address these issues, this paper curates a large-scale protein-text paired dataset called ProtAnno with a property-driven sampling strategy, and introduces a novel function-informed protein pre-training paradigm. Specifically, the sampling strategy determines selecting probability based on the sample confidence and property coverage, balancing the data quality and data quantity in face of large-scale noisy data. Furthermore, motivated by significance of the protein specific functional mechanism, the proposed paradigm explicitly model protein static and dynamic functional segments by two segment-wise pre-training objectives, injecting fine-grained information in a function-informed manner. Leveraging all these innovations, we develop ProtCLIP, a multi-modality foundation model that comprehensively represents function-aware protein embeddings. On 22 different protein benchmarks within 5 types, including protein functionality classification, mutation effect prediction, cross-modal transformation, semantic similarity inference and protein-protein interaction prediction, our ProtCLIP consistently achieves SOTA performance, with remarkable improvements of 75% on average in five cross-modal transformation benchmarks, 59.9% in GO-CC and 39.7% in GO-BP protein function prediction. The experimental results verify the extraordinary potential of ProtCLIP serving as the protein multi-modality foundation model. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: dataset and Model Development

arXiv

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arXiv 2024年

作者： Jiang, Yuncheng Hu, Yiwen Zhang, Zixun Wei, Jun Feng, Chun-Mei Tang, Xuemei Wan, Xiang Liu, Yong Cui, Shuguang Li, Zhen SSE The Chinese University of Hong Kong Shenzhen China Shenzhen Future Network of Intelligence Insititute China Institute of High Performance Computing Agency for Science Technology and Research Singapore Shenzhen Research Institute of Big Data Shenzhen China Affiliated Hospital of North Sichuan Medical College Sichuan China

Endorectal ultrasound (ERUS) is an important imaging modality that provides high reliability for diagnosing the depth and boundary of invasion in colorectal cancer. However, the lack of a large-scale ERUS dataset with high-quality annotations hinders the development of automatic ultrasound diagnostics. In this paper, we collected and annotated the first benchmark dataset that covers diverse ERUS scenarios, i.e. colorectal cancer segmentation, detection, and infiltration depth staging. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames. Based on this dataset, we further introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR). ASTR is designed based on three considerations: scanning mode discrepancy, temporal information, and low computational complexity. For generalizing to different scanning modes, the adaptive scanning-mode augmentation is proposed to convert between raw sector images and linear scan ones. For mining temporal information, the sparse-context transformer is incorporated to integrate inter-frame local and global features. For reducing computational complexity, the sparse-context block is introduced to extract contextual features from auxiliary frames. Finally, on the benchmark dataset, the proposed ASTR model achieves a 77.6% Dice score in rectal cancer segmentation, largely outperforming previous state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Diagnosis

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Augmented Reality-Assisted Pediatric Surgery: A Machine Learning-Based Approach for Enhancing Accuracy

Augmented Reality-Assisted Pediatric Surgery: A Machine Lear...

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Advances in Computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumedha Bhardwaj Swapna Narla Md Nurul Absur Shuvo Dutta J B Hyzil S M Krishna Ganesh Department of School of Computing DIT University Dehradun Uttarakhand India Tek Yantra Inc California USA Computer Science CUNY Graduate Center New York New York USA Department of Physics Western Michigan University USA Department of Zoology Govt College for Women Affiliated to University of Kerala Thiruvananthapuram Department of Artificial Intelligence and Data Science New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

关键词： Pediatrics Accuracy Navigation Instruments Surgery Medical services Information age Object recognition Augmented reality X-ray imaging

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