Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures. Developing an efficient method for rare disease detection is a crucial first step towards subsequent clinical research. In this paper, we present a semi-supervised learning framework for rare disease detection using generative adversarial networks. Our method takes advantage of the large amount of unlabeled data for disease detection and achieves the best results in terms of precision-recall score compared to baseline techniques.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
University of Pennsylvania（宾夕法尼亚大学）；Center for AI and Data Science for Integrated Diagnostics（综合诊断人工智能与数据科学中心）；Center for Biomedical Image Computing and Analytics（生物医学图像计算与分析中心）
Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkers
School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）；Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University（佐治亚州立大学神经影像学与数据科学转化研究三机构中心（TReNDS））；College of Pharmacy, University of Florida（佛罗里达大学药学院）
The RareDis corpus: a corpus annotated with rare diseases, their signs and symptoms
Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Hospital Fundación Jiménez Díaz e Instituto de Investigación, FJD（希门尼斯·迪亚斯基金会医院与研究所）；CIEMAT（西班牙能源、环境与技术研究中心）；Centre for Biomedical Network Research on Rare Diseases (CIBERER)（罕见病生物医学网络研究中心）
A Weakly Supervised Transformer for Rare Disease Diagnosis and Subphenotyping from EHRs with Pulmonary Case Studies
Harvard T.H. Chan School of Public Health（哈佛T.H. 洪学校公共卫生学院）；Harvard Medical School（哈佛医学院）；Bentley University（伯顿大学）；Columbia University（哥伦比亚大学）；Boston Children’s Hospital（波士顿儿童医院）；Computational Health Informatics Program（计算健康信息学计划）；Department of Pediatrics（儿科部门）；Brigham and Women’s Hospital（布里奇沃特医院）
Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts
Universidad Carlos III de Madrid（卡洛斯三世大学）；Hospital Fundación Jiménez Díaz e Instituto de Investigación, FJD（希门尼斯·迪亚斯基金会医院及研究所（FJD））；CIEMAT（环境、能源与技术研究中心）；Centre for Biomedical Network Research on Rare Diseases (CIBERER)（罕见疾病生物医学网络研究中心（CIBERER））
HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition
Technical University of Munich（慕尼黑工业大学）；Helmholtz Center Munich（亥姆霍兹慕尼黑中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；Aalto University（阿尔托大学）
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation
National University of Science and Technology POLITEHNICA Bucharest（布加勒斯特国立科技理工大学）；National Institute for Research and Development in Informatics - ICI Bucharest（罗马尼亚国家信息研究与发展研究所（ICI布加勒斯特））；Academy of Romanian Scientists（罗马尼亚科学家学院）
Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology, Thammasat University（泰国朱拉隆功大学国际技术学院）
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University（深圳大学）；Renmin University of China（中国人民大学）；Central South University（中南大学）
Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis
Department of Computer Science, Technical University of Munich（技术大学慕尼黑计算机科学系）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML））；Johns Hopkins University, Baltimore, Maryland, USA（约翰霍普金斯大学）
RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records
Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Department of Pediatrics, University of Illinois College of Medicine Peoria（伊利诺伊大学皮奥里亚医学院儿科系）
One Score, Two Decisions: Selective Prediction on the Rare-Disease Tail
School of Health & Wellbeing, University of Glasgow（格拉斯哥大学健康与福祉学院）；Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属第六人民医院）；School of Life Science and Technology, University of Electronic Science and Technology of China（电子科技大学生命科学与技术学院）
Multi-scale Frequency-Aware Adversarial Network for Parkinson's Disease Assessment Using Wearable Sensors
National Pilot School of Software Yunnan University（云南大学软件试点学校）；School of Computer Science University of Sheffield（计算机科学学院谢菲尔德大学）；Department of Computing Xi'an JiaoTong-Liverpool University（计算学院西安交通大学-利物浦大学）
ShrimpXNet: A Transfer Learning Framework for Shrimp Disease Classification with Augmented Regularization, Adversarial Training, and Explainable AI
East West University（东西大学）；Daffodil International University（达福迪尔国际大学）；North South University（北南大学）；University of Information Technology & Sciences（信息技术与科学大学）
How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?
Mannheim University of Applied Science（曼海姆应用科技大学）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；DZHK (German Centre for Cardiovascular Research)（德国心血管研究中心）；German Heart Centre Munich（慕尼黑德国心脏中心）；Hannover Medical School（汉诺威医学院）；Heidelberg University Hospital（海德堡大学医院）
Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation
Institute for Bioinformatics and Medical Informatics, University of Tübingen（蒂宾根大学生物信息学与医学信息学研究所）；Medical Data Privacy and Privacy-Preserving Machine Learning, University of Tübingen（蒂宾根大学医学数据隐私与隐私增强机器学习研究所）；University Hospital Aachen（亚琛大学医院）；Delft University of Technology（代尔夫特理工大学）
A swap-adversarial framework for domain generalization in pre- and post-stimulation Parkinsonian state classification
Department of Computer Science and Engineering, Incheon National University（仁川国立大学计算机科学与工程系）；Department of Nanobioengineering, Incheon National University（仁川国立大学纳米生物工程系）；Center for Brain-Machine Interface, Incheon National University（仁川国立大学脑机接口中心）；Department of Artificial Intelligence, Inha University（仁荷大学人工智能系）
GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Text
Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia（雷蒙德·G·佩尔曼细胞与分子治疗中心，费城儿童医院）；Department of Mathematics, University of Pennsylvania（数学系，宾夕法尼亚大学）；Department of Biomedical Informatics, Columbia University Irving Medical Center（生物医学信息学系，哥伦比亚大学伊万斯医疗中心）；Department of Human Genetics, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA（人类遗传学系，纽约州发育障碍基础研究机构，纽约州史泰登岛）；Division of Human Genetics, Children’s Hospital of Philadelphia（人类遗传学部，费城儿童医院）；Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School（儿科系，波士顿儿童医院，哈佛医学院）；Biology PhD Program, The Graduate Center, The City University of New York（生物学博士项目，纽约市立大学研究生中心）；Department of Genetics, Perelman School of Medicine, University of Pennsylvania（遗传学系，宾夕法尼亚大学佩尔曼医学学院）；Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania（儿科系，宾夕法尼亚大学佩尔曼医学学院）；Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania（病理学与实验室医学系，宾夕法尼亚大学佩尔曼医学学院）
Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks
Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data
University of Connecticut（康涅狄格大学）；University of Connecticut Health Center (UCHC)（康涅狄格大学健康中心）；Weill Cornell Medical College, Cornell University（康奈尔大学威尔医学院）
GraphRareBench: An Auditable Graph-Evidence Benchmark for Phenotype-Driven Rare-Disease Diagnosis
Xiamen University（厦门大学）；National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康医疗大数据国家研究院）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；School of Medicine, Xiamen University（厦门大学医学院）；The First Affiliated Hospital of Xiamen University（厦门大学附属第一医院）
