A key step in medical diagnosis is giving the patient a universally recognized label (e.g. Appendicitis) which essentially assigns the patient to a class(es) of patients with similar body failures. However, two patients having the same disease label(s) with high probability may still have differences in their feature manifestation patterns implying differences in the required treatments. Additionally, in many cases, the labels of the primary diagnoses leave some findings unexplained. Medical diagnosis is only partially about probability calculations for label X or Y. Diagnosis is not complete until the patient overall situation is clinically understood to the level that enables the best therapeutic decisions. Most machine learning models are data centric models, and evidence so far suggest they can reach expert level performance in the disease labeling phase. Nonetheless, like any other mathematical technique, they have their limitations and applicability scope. Primarily, data centric algorithms are knowledge blind and lack anatomy and physiology knowledge that physicians leverage to achieve complete diagnosis. This article advocates to complement them with intelligence to overcome their inherent limitations as knowledge blind algorithms. Machines can learn many things from data, but data is not the only source that machines can learn from. Historic patient data only tells us what the possible manifestations of a certain body failure are. Anatomy and physiology knowledge tell us how the body works and fails. Both are needed for complete diagnosis. The proposed Double Deep Learning approach, along with the initiative for Medical Wikipedia for Smart Machines, leads to AI diagnostic support solutions for complete diagnosis beyond the limited data only labeling solutions we see today. AI for medicine will forever be limited until their intelligence also integrates anatomy and physiology.
ISTI-CNR（意大利国家研究委员会信息科学与技术研究所）；University of Pisa（比萨大学）；Sorbonne Universite（索邦大学）
Explainable AI consists in developing mechanisms allowing for an interaction between decision systems and humans by making the decisions of the formers understandable. This is particularly important in sensitive contexts like in the medical domain. We propose a use case study, for skin lesion diagnosis, illustrating how it is possible to provide the practitioner with explanations on the decisions of a state of the art deep neural network classifier trained to characterize skin lesions from examples. Our framework consists of a trained classifier onto which an explanation module operates. The l…
Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Institute of Artificial Intelligence Research, Jiangsu Second Normal University（江苏第二师范学院人工智能研究所）；Digital Research Institute, ENN Group（ENN集团数字研究院）；School of Computing, Engineering and Mathematical Sciences, La Trobe University（拉筹伯大学计算、工程与数学科学学院）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）
Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosis
University of Southern California（美国南加州大学）；Georgia Institute of Technology（佐治亚理工学院）；Sion Hospital（Sion医院）；Clinical Psychotherapist（临床心理治疗师）；Sophia College for Women（Sophia女子学院）；Indian Institute of Technology Bombay（印度班加罗尔理工学院）
PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling
Vetsuisse Faculty, University of Bern（伯尔尼大学兽医系）；Case Western Reserve University（凯斯西储大学）；Toth Technology LLC（托思科技有限责任公司）；Duke University（杜克大学）；Institute of Pathology, Lausanne University Hospital（洛桑大学医院病理研究所）；Bern Center for Precision Medicine, University of Bern（伯尔尼大学伯尔尼精准医学中心）；Emory University（埃默里大学）；Georgia Institute of Technology（佐治亚理工学院）；Atlanta Veterans Medical Center（亚特兰大退伍军人医疗中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；University Hospital of Geneva（日内瓦大学医院）
本文发布开源数字病理标注工具 PatchSorter，通过结合深度学习与直观 Web 界面，在超过 100,000 个对象上实现较人工无辅助标注逾 7 倍的速度提升且几乎不损失准确性。
National University of Singapore（新加坡国立大学）；National University Hospital（国立大学医院）；Zhejiang University（浙江大学）
In this paper, we propose a novel semi-supervised learning (SSL) framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the potential of medical image SSL models: (1) BoostMIS can adaptively leverage the cluster assumption and consistency regularization of the unlabeled data according to the current learning status. This strategy can adaptively generate one-hot "hard" labels converted from task model predictions for better task model training. (2) For the unselected unlabeled images with low confidence, we introduce an Active learning (AL) …
Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet
A. K. Choudhury School of Information Technology, University of Calcutta（加尔各答大学A. K.乔杜里信息技术学院）；Rammohan College, University of Calcutta（加尔各答大学拉莫汉学院）；Indian Statistical Institute（印度统计研究所）
本研究提出基于改进注意力 U-Net 架构和新型掩码逻辑的方法，用于 3D 腰椎 MRI 数据的椎骨全景分割与标注，实现 99.5% 的高准确率，显著推进该领域最先进技术。
University of Pittsburgh（匹兹堡大学）；University of Notre Dame（圣母大学）；George Mason University（乔治梅森大学）；University of New Mexico（新墨西哥大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare. These models rely on training with a tremendous amount of labeled data to achieve high accuracy. However, for medical applications such as dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients, and each device only has a limited amount of data. Directly learning from limited data greatly deteriorates the performance of learned models. Federated learning (FL) can train models by using data distri…
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Siemens Healthcare GmbH（西门子医疗有限公司）；Das Radiologische Zentrum - Radiology Center（放射学中心）
With coronary artery disease (CAD) persisting to be one of the leading causes of death worldwide, interest in supporting physicians with algorithms to speed up and improve diagnosis is high. In clinical practice, the severeness of CAD is often assessed with a coronary CT angiography (CCTA) scan and manually graded with the CAD-Reporting and Data System (CAD-RADS) score. The clinical questions this score assesses are whether patients have CAD or not (rule-out) and whether they have severe CAD or not (hold-out). In this work, we reach new state-of-the-art performance for automatic CAD-RADS scori…
Institute of Statistics and Big Data（统计与大数据研究院）；Renmin University of China（中国人民大学）；MetaAI（元宇宙人工智能实验室）；The University of British Columbia（不列颠哥伦比亚大学）；RIKEN（理化学研究所）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models
Monash University（莫纳什大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye and Ear Hospital（皇家维多利亚眼耳医院）；The University of Melbourne（墨尔本大学）；The Hong Kong Polytechnic University（香港理工大学）；Centre for Eye and Vision Research（眼与视觉研究中心）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Airdoc-Monash Research Lab（Airdoc-莫纳什研究实验室）
