Machine Learning (ML) is widely used to automatically extract meaningful information from Electronic Health Records (EHR) to support operational, clinical, and financial decision-making. However, ML models require a large number of annotated examples to provide satisfactory results, which is not possible in most healthcare scenarios due to the high cost of clinician-labeled data. Active Learning (AL) is a process of selecting the most informative instances to be labeled by an expert to further train a supervised algorithm. We demonstrate the effectiveness of AL in multi-label text classification in the clinical domain. In this context, we apply a set of well-known AL methods to help automatically assign ICD-9 codes on the MIMIC-III dataset. Our results show that the selection of informative instances provides satisfactory classification with a significantly reduced training set (8.3\% of the total instances). We conclude that AL methods can significantly reduce the manual annotation cost while preserving model performance.
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation
Hasso Plattner Institute, University of Potsdam（波茨坦大学哈索·普拉特纳研究所）；Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山哈索·普拉特纳数字健康研究所）
Active Learning with Imperfect Labels: Optimal Labeler Assignment and Sample Selection
School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332, USA（工业与系统工程学院 佐治亚理工学院 阿拉巴马州亚特兰大 30332, 美国）；Ford Motor Company（福特汽车公司）
WaveFuse-AL: Cyclical and Performance-Adaptive Multi-Strategy Active Learning for Medical Images
Department of Computer Science \& Engineering, Indian Institute of Technology Ropar, India Department of Biomedical Engineering, Indian Institute of Technology Ropar, India
ActiveMedAgent: Cost-Aware Trajectory Learning for Multimodal Medical Diagnosis
Washington University in St. Louis（圣路易斯华盛顿大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Arizona State University（亚利桑那州立大学）；Yale University（耶鲁大学）；Amazon（亚马逊公司）
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
Center for Imaging Science, Rochester Institute of Technology（罗切斯特理工学院成像科学中心）；University of Aberdeen（阿伯丁大学）；Biomedical Engineering, Rochester Institute of Technology（罗切斯特理工学院生物医学工程系）
Dataset-Aware Cold-Start Active Learning for Annotation-Efficient 3D Medical Image Segmentation
IADI (U1254)（IADI（U1254））；Inserm and Université de Lorraine（Inserm和洛林大学）；CIC-IT 1433；Inserm, Université de Lorraine（Inserm和洛林大学）；CHRU Nancy（南锡大学医院）；Université de Lorraine（洛林大学）；CHRU-Nancy（南锡大学医院）；Pôle de la femme（妇女中心）
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification
Turner Institute for Brain and Mental Health, Monash University（莫纳什大学特纳大脑与心理健康研究所）；Wellcome Centre for Human Neuroimaging, UCL（伦敦大学学院惠康人类神经成像中心）；Information Technology University (ITU) Punjab（旁遮普信息技术大学）
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China（电子科技大学光电科学与工程学院）；Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；The Hospital for Sick Children (SickKids)（SickKids 儿童医院（病童医院））
