Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these works have primarily focused on classification accuracy, ignoring the important role of uncertainty quantification. Empirically, neural networks are often miscalibrated and overconfident in their predictions. This miscalibration could be problematic in any automatic decision-making system, but we focus on the medical field in which neural network miscalibration has the potential to lead to significant treatment errors. We propose a novel calibration approach that maintains the overall classification accuracy while significantly improving model calibration. The proposed approach is based on expected calibration error, which is a common metric for quantifying miscalibration. Our approach can be easily integrated into any classification task as an auxiliary loss term, thus not requiring an explicit training round for calibration. We show that our approach reduces calibration error significantly across various architectures and datasets.
DOMINO: Domain-aware Model Calibration in Medical Image Segmentation
University of Florida（佛罗里达大学）；Herbert Wertheim College of Engineering（赫伯特·韦特海姆工程学院）；McKnight Brain Institute（麦克奈特脑研究所）；College of Public Health and Health Professions（公共卫生与健康职业学院）；College of Medicine（医学院）；United States Air Force Research Laboratory（美国空军研究实验室）
Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
Johns Hopkins University, School of Medicine（约翰霍普金斯大学医学院）；Massachusetts Institute of Technology（麻省理工学院）；Microsoft Healthcare & Life Sciences（微软医疗健康与生命科学）
Towards unraveling calibration biases in medical image analysis
Hospital Italiano de Buenos Aires（布宜诺斯艾利斯意大利医院）；Universidad Tecnológica Nacional（国立技术大学）；Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET)（信号、系统与计算智能研究所sinc(i)（FICH-UNL/阿根廷国家科学与技术研究理事会））
Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification
Centre for Intelligent Machines, McGill University, Montreal, Canada（智能机器中心，麦吉尔大学，加拿大）；Mila -- Quebec AI Institute, Montreal, Canada（魁北克人工智能研究所）；Stanford University, Stanford, USA（斯坦福大学）；University of Copenhagen, Copenhagen, Denmark（哥本哈根大学）
EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Applied Sciences Western Switzerland (HES-SO)（瑞士西部应用科学大学（HES-SO））
Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation
Tryolabs（Tryolabs公司）；Research institute for signals, systems and computational intelligence, sinc(i), FICH-UNL / CONICET（信号、系统与计算智能研究所（sinc(i)，国立拉普拉塔大学计算机科学学院/阿根廷国家科学与技术研究理事会））；LIVIA, ETS Montreal（蒙特利尔高等技术学院LIVIA实验室）；International Laboratory on Learning Systems (ILLS)（国际学习系统实验室（ILLS））
Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Zhengzhou University（郑州大学）；Central South University（中南大学）；Xinjiang University（新疆大学）
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；University Medical Center Heidelberg（海德堡大学医院）；Helmholtz Imaging（亥姆霍兹成像机构）
MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis
ShanghaiTech University（上海科技大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
Multi-modal MRI Translation via Evidential Regression and Distribution Calibration
Fudan University（复旦大学）；University of Cambridge（剑桥大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Fuzhou University（福州市大学）；Shanghai Jiao Tong University（上海交通大学）
Brain Tumor Classification using Vision Transformer with Selective Cross-Attention Mechanism and Feature Calibration
K.N. Toosi University of Technology（霍梅尼国际大学（K.N. Toosi理工大学））；Indiana University-Purdue University（印第安纳大学-普渡大学）；Shahed university（沙希德大学）；University of Cincinnati（辛辛那提大学）
Does Bigger Mean Better? Comparitive Analysis of CNNs and Biomedical Vision Language Modles in Medical Diagnosis
University of Texas at Dallas（德克萨斯大学达拉斯分校）；Independent Researcher（独立研究者）；Duke University（杜克大学）；University of Michigan Ann Arbor（密歇根大学安娜堡分校）；Georgia Institute of Technology（佐治亚理工学院）；University of California, Irvine（加州大学 Irvine 分校）
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
McGill University（麦吉尔大学）；Centre for Intelligent Machines（智能机器中心）；Mila – Quebec AI Institute（魁北克人工智能研究所）；University of Copenhagen（哥本哈根大学）；Department of Public Health, Section for Health Data Science and AI（公共卫生系，健康数据科学与人工智能部门）
Governance-Ready Small Language Models for Medical Imaging: Prompting, Abstention, and PACS Integration
Department of Data Science, University of Southern California（数据科学系，南加州大学）；Department of Electrical and Computer Engineering, Carnegie Mellon University（电气与计算机工程系，卡内基梅隆大学）；Department of Computer Science and Engineering, Santa Clara University（计算机科学与工程系，圣克拉拉大学）；Department of Applied Mathematics, University of Washington（应用数学系，华盛顿大学）；School of Computer Science, Georgia Institute of Technology（计算机科学学院，佐治亚理工学院）
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training
School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院）；Shandong Artificial Intelligence Institute, Qilu University of Technology（齐鲁工业大学山东人工智能研究院）；School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）
TGC-Net: A Structure-Aware and Semantically-Aligned Framework for Text-Guided Medical Image Segmentation
School of Computer Science, Wuhan University（武汉大学计算机学院）；Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science（同济大学同济医学学院骨科部，华中科技大学）
