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arXiv 2024年

作者： Gao, Wanling Liu, Yuan Yu, Zhuoming Cui, Dandan Liu, Wenjing Liang, Xiaoshuang Zhao, Jiahui Xie, Jiyue Li, Hao Ma, Li Ye, Ning Kang, Yumiao Luo, Dingfeng Pan, Peng Huang, Wei Liu, Zhongmou Hu, Jizhong Huang, Fan Zhao, Gangyuan Jiang, Chongrong Wei, Tianyi Zhang, Zhifei Huang, Yunyou Zhan, Jianfeng Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China International Open Benchmark Council University of Chinese Academy of Sciences Beijing100086 China Guilin Medical University Guilin541100 China Affiliated Hospital of Guilin Medical University Guilin541000 China Xing An County People’s Hospital Guilin541300 China Meng Shan County People’s Hospital Wuzhou543000 China XuanJi Technology Co. Ltd. Guilin541000 China Guilin People’s Hospital Guilin541000 China Yong Fu County People’s Hospital Guilin541000 China Ling Chuan County People’s Hospital Guilin541000 China The Second Affiliated Hospital of Guilin Medical University Guilin541000 China Quan Zhou County People’s Hospital Guilin541000 China Guan Yang County People’s Hospital Guilin541000 China International College Guangxi University Nanning530004 China Capital Medical University Beijing100069 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China

Artificial Intelligence (AI) plays a crucial role in medical field and has the potential to revolutionize healthcare practices. However, the success of AI models and their impacts hinge on the synergy between AI and medical specialists, with clinicians assuming a dominant role. Unfortunately, the intricate dynamics and interactions between AI and clinicians remain undiscovered and thus hinder AI from being translated into medical practice. To address this gap, we have curated a groundbreaking database called ***. This database is the first of its kind for studying the interactions between AI and clinicians. It derives from 7,500 collaborative diagnosis records on a life-threatening medical emergency – Sepsis – from 14 medical centers across China. For the patient cohorts well-chosen from MIMIC databases, the AI-related information comprises the model property, feature input, diagnosis decision, and inferred probabilities of sepsis onset presently and within next three hours. The clinician-related information includes the viewed examination data and sequence, viewed time, preliminary and final diagnosis decisions with or without AI assistance, and recommended treatment. © 2024, CC BY-NC-ND.

关键词： Database systems

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Asym-Unet: An Asymmetric U-Shape Network for Lesion Segmentation of Breast Cancer

SSRN

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SSRN 2024年

作者： Liu, Jia Shao, Jun Xu, Sen Tang, Zhiyong Liu, Weiquan Li, Zeshuai Wang, Tao Bian, Xuesheng Department of Information Engineering Yancheng Institute of Technology Jiangsu Yancheng224007 China Fujian Key Lab of Sensing and Computing for Smart Cities School of Informatics Xiamen University Fujian Xiamen361000 China Department of Ultrasound The First People’s Hospital of Yancheng Jiangsu Yancheng224007 China Department of Electrical Engineering Shanxi Electric Power Technical College Shanxi Taiyuan030020 China

Precise segmentation of breast ultrasound images is crucial in early breast cancer screening. However, the diverse tumor morphology, blurred boundaries, and the similar grey intensity distribution between lesions and normal tissues in breast ultrasound images make it challenging to segment lesions accurately. To tackle these issues, we propose the Asym-Unet, an asymmetric U-shaped network, to segment the breast lesion in ultrasound images accurately and reliably. In detail, our Asym-Unet consists of a multi-branch residual encoder, an external attention module, and a boundary detection module. The multi-branch residual encoder effectively captures the discriminative representation of breast lesions for subsequent segmentation tasks. The external attention module further guides the network to focus on the lesion areas while filtering out interference information for diagnosis. Additionally, to further improve the network's performance, we introduce the boundary detection module for refining the boundary of the predicted masks. The boundary detection module offers extra supervision and guidance through a multi-level prediction layer. We compared our Asym-Unet with ten other segmentation methods on two publicly available datasets. The results demonstrate that the network performs state-of-the-art in the breast tumor segmentation task. © 2024, The Authors. All rights reserved.

关键词： Diagnosis

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Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer

arXiv

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arXiv 2023年

作者： Dong, Hexin Yao, Jiawen Tang, Yuxing Yuan, Mingze Xia, Yingda Zhou, Jian Lu, Hong Zhou, Jingren Dong, Bin Lu, Le Liu, Zaiyi Zhang, Li Shi, Yu Zhang, Ling DAMO Academy Alibaba Group China Peking University China Hupan Lab Hangzhou310023 China Sun Yat-sen University Cancer Center China South China Hospital Shenzhen University China Tianjin Medical University Cancer Institute and Hospital China Peking University Changsha Institute for Computing and Digital Economy China Guangdong Provincial People’s Hospital China Shengjing Hospital China

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients. However, current prognostic prediction methods fail to explicitly and accurately investigate relationships between the tumor and nearby important vessels. This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients, adopting it as a major feature for prognosis prediction. Besides, different from existing models that used CNNs or LSTMs to exploit tumor enhancement patterns on dynamic contrast-enhanced CT imaging, we improved the extraction of dynamic tumor-related texture features in multi-phase contrast-enhanced CT by fusing local and global features using CNN and transformer modules, further enhancing the features extracted across multi-phase CT images. We extensively evaluated and compared the proposed method with existing methods in the multi-center (n=4) dataset with 1,070 patients with PDAC, and statistical analysis confirmed its clinical effectiveness in the external test set consisting of three centers. The developed risk marker was the strongest predictor of overall survival among preoperative factors and it has the potential to be combined with established clinical factors to select patients at higher risk who might benefit from neoadjuvant therapy. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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A Single Channel-Based Neonatal Sleep-Wake Classification using Hjorth Parameters and Improved Gradient Boosting

arXiv

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arXiv 2024年

作者： Arslan, Muhammad Mubeen, Muhammad Abbasi, Saadullah Farooq Khan, Muhammad Shahbaz Boulila, Wadii Ahmad, Jawad Department of Computer Science Loyola University ChicagoIL60660 United States Department of Computer Science University of People PasadenaCA91101 United States Department of Electronic Electrical and Systems Engineering University of Birmingham BirminghamB15 2TT United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH105DT United Kingdom RIOTU Lab Prince Sultan University Riyadh12435 Saudi Arabia

Sleep plays a crucial role in neonatal development. Monitoring the sleep patterns in neonates in a Neonatal Intensive Care Unit (NICU) is imperative for understanding the maturation process. While polysomnography (PSG) is considered the best practice for sleep classification, its expense and reliance on human annotation pose challenges. Existing research often relies on multichannel EEG signals;however, concerns arise regarding the vulnerability of neonates and the potential impact on their sleep quality. This paper introduces a novel approach to neonatal sleep stage classification using a single-channel gradient boosting algorithm with Hjorth features. The gradient boosting parameters are fine-tuned using random search cross-validation (randomsearchCV), achieving an accuracy of 82.35% for neonatal sleep-wake classification. Validation is conducted through 5-fold cross-validation. The proposed algorithm not only enhances existing neonatal sleep algorithms but also opens avenues for broader applications. © 2024, CC BY.

关键词： Neonatal monitoring

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Decision-based black-box attack against vision transformers via patch-wise adversarial removal 22

Decision-based black-box attack against vision transformers ...

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Yucheng Shi Yahong Han Yu-an Tan Xiaohui Kuang College of Intelligence and Computing and Tianjin Key Lab of Machine Learning Tianjin University Tianjin China and Engineering Research Center of City Intelligence and Digital Governance Ministry of Education of the People's Republic of China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9781713871088

Vision transformers (ViTs) have demonstrated impressive performance and stronger adversarial robustness compared to Convolutional Neural Networks (CNNs). On the one hand, ViTs' focus on global interaction between individual patches reduces the local noise sensitivity of images. On the other hand, the neglect of noise sensitivity differences between image regions by existing decision-based attacks further compromises the efficiency of noise compression, especially for ViTs. Therefore, validating the black-box adversarial robustness of ViTs when the target model can only be queried still remains a challenging problem. In this paper, we theoretically analyze the limitations of existing decision-based attacks from the perspective of noise sensitivity difference between regions of the image, and propose a new decision-based black-box attack against ViTs, termed Patch-wise Adversarial Removal (PAR). PAR divides images into patches through a coarse-to-fine search process and compresses the noise on each patch separately. PAR records the noise magnitude and noise sensitivity of each patch and selects the patch with the highest query value for noise compression. In addition, PAR can be used as a noise initialization method for other decision-based attacks to improve the noise compression efficiency on both ViTs and CNNs without introducing additional calculations. Extensive experiments on three datasets demonstrate that PAR achieves a much lower noise magnitude with the same number of queries.

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Region Proposal Network with Graph Prior and Iou-Balance Loss for Landmark Detection in 3D Ultrasound

Region Proposal Network with Graph Prior and Iou-Balance Los...

引用

IEEE International Symposium on Biomedical Imaging

作者： Chaoyu Chen Xin Yang Ruobing Huang Wenlong Shi Shengfeng Liu Mingrong Lin Yuhao Huang Yong Yang Yuanji Zhang Huanjia Luo Yankai Huang Yi Xiong Dong Ni Medical UltraSound Image Computing (MUSIC) Lab Shenzhen University Shenzhen China Luohu People's Hosptial Shenzhen China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

3D ultrasound (US) can facilitate detailed prenatal examinations for fetal growth monitoring. To analyze a 3D US volume, it is fundamental to identify anatomical landmarks of the evaluated organs accurately. Typical deep learning methods usually regress the coordinates directly or involve heatmap-matching. However, these methods struggle to deal with volumes with large sizes and the highly-varying positions and orientations of fetuses. In this work, we exploit an object detection framework to detect landmarks in 3D fetal facial US volumes. By regressing multiple parameters of the landmark-centered bounding box (B-box) with a strict criteria, the proposed model is able to pinpoint the exact location of the targeted landmarks. Specifically, the model uses a 3D region proposal network (RPN) to generate 3D candidate regions, followed by several 3D classification branches to select the best candidate. It also adopts an IoU-balance loss to improve communications between branches that benefit the learning process. Furthermore, it leverage a distance-based graph prior to regularize the training and helps to reduce false positive predictions. The performance of the proposed framework is evaluated on a 3D US dataset to detect five key fetal facial landmarks. Results showed the proposed method outperforms some of the state-of-the-art methods in efficacy and efficiency.

关键词： Three-dimensional displays Ultrasonic imaging Task analysis Object detection Proposals Face Biomedical imaging

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Aligning Recommendation and Conversation via Dual Imitation

arXiv

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arXiv 2022年

作者： Zhou, Jinfeng Wang, Bo Huang, Minlie Zhao, Dongming Huang, Kun He, Ruifang Hou, Yuexian College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Communication Content Cognition People’s Daily Online Beijing China The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China AI Lab China Mobile Communication Group Tianjin Co. Ltd. China

Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics. © 2022, CC BY.

关键词： Knowledge graph

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Generation of Circulating Cavity Magnon Polaritons

引用

Physical Review Applied 2023年第1期19卷 014030-014030页

作者： Jeremy Bourhill Weichao Yu Vincent Vlaminck Gerrit E. W. Bauer Giuseppe Ruoso Vincent Castel Quantum Technologies and Dark Matter Research Lab Department of Physics University of Western Australia 35 Stirling Highway Crawley Western Australia 6009 Australia State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 People’s Republic of China Zhangjiang Fudan International Innovation Center Fudan University Shanghai 201210 People’s Republic of China IMT Atlantique Technopole Brest-Iroise CS 83818 Brest Cedex 3 29238 France Laboratoire des Sciences et Technologies de l'Information de la Communication et de la Connaissance (UMR 6285) CNRS Technopole Brest-Iroise CS 83818 Brest Cedex 3 29238 France World Premier International Research Center Initiative (WPI) – Advanced Institute of Materials Research (AIMR) and Institute for Materials Research (IMR) Tohoku University Sendai 980-8577 Japan Kavli Institute for Theoretical Sciences University of the Chinese Academy of Sciences Beijing 10090 People’s Republic of China Zernike Institute for Advanced Materials Groningen University 9700 AB Groningen Kingdom of the Netherlands Istituto Nazionale di Fisica Nucleare (INFN) Laboratori Nazionali di Legnaro Viale Dell’Università 2 Legnaro Padova 35020 Italy

We experimentally realize circularly polarized unidirectional cavity magnon polaritons in a torus-shaped microwave cavity loaded by a small magnetic sphere. At special positions, the clockwise and counterclockwise modes are circularly polarized, such that only one of them couples to the magnet, which breaks the mode degeneracy. We reveal the chiral nature of the spectral energy and angular-momentum flow by observing and modeling nonreciprocities of the microwave scattering matrix.

关键词： Angular momentum of light Magnons Polaritons Polarization of light Spin waves RF, microwave, & terahertz sources

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Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification

arXiv

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arXiv 2024年

作者： Zhou, Xinrui Huang, Yuhao Dou, Haoran Chen, Shijing Chang, Ao Liu, Jia Long, Weiran Zheng, Jian Xu, Erjiao Ren, Jie Huang, Ruobing Cheng, Jun Xue, Wufeng Ni, Dong The National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China The Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China The School of Computing University of Leeds United Kingdom The Department of Computer Science School of Engineering University of Manchester United Kingdom The Third Affiliated Hospital of Sun Yat-sen University Guangzhou China The Longgang District People’s Hospital of Shenzhen The Second Affiliated Hospital of The Chinese University of Hong Kong Shenzhen China The Eighth Affiliated Hospital of Sun Yat-sen University Shenzhen China

In the medical field, the limited availability of large-scale datasets and labor-intensive annotation processes hinder the performance of deep models. Diffusion-based generative augmentation approaches present a promising solution to this issue, having been proven effective in advancing downstream medical recognition tasks. Nevertheless, existing works lack sufficient semantic and sequential steerability for challenging video/3D sequence generation, and neglect quality control of noisy synthesized samples, resulting in unreliable synthetic databases and severely limiting the performance of downstream tasks. In this work, we present Ctrl-GenAug, a novel and general generative augmentation framework that enables highly semantic- and sequential-customized sequence synthesis and suppresses incorrectly synthesized samples, to aid medical sequence classification. Specifically, we first design a multimodal conditions-guided sequence generator for controllably synthesizing diagnosis-promotive samples. A sequential augmentation module is integrated to enhance the temporal/stereoscopic coherence of generated samples. Then, we propose a noisy synthetic data filter to suppress unreliable cases at semantic and sequential levels. Extensive experiments on 3 medical datasets, using 11 networks trained on 3 paradigms, comprehensively analyze the effectiveness and generality of Ctrl-GenAug, particularly in underrepresented high-risk populations and out-domain conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Diagnosis

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