In this work we examine the performance enhancement in classification of medical imaging data when image features are combined with associated non-image data. We compare the performance of eight state-of-the-art deep neural networks in classification tasks when using only image features, compared to when these are combined with patient metadata. We utilise transfer learning with networks pretrained on ImageNet used directly as feature extractors and fine tuned on the target domain. Our experiments show that performance can be significantly enhanced with the inclusion of metadata and use interpretability methods to identify which features lead to these enhancements. Furthermore, our results indicate that the performance enhancement for natural medical imaging (e.g. optical images) benefit most from direct use of pre-trained models, whereas non natural images (e.g. representations of non imaging data) benefit most from fine tuning pre-trained networks. These enhancements come at a negligible additional cost in computation time, and therefore is a practical method for other applications.
Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging
Massachusetts Institute of Technology（麻省理工学院）；University of California, San Diego（加州大学圣地亚哥分校）；University of Washington（华盛顿大学）；University of Washington School of Medicine（华盛顿大学医学院）
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）
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
Harvard AI and Robotics Lab（哈佛人工智能与机器人实验室）；Harvard University（哈佛大学）；Harvard Ophthalmology AI lab（哈佛眼科人工智能实验室）；University of Central Florida（佛罗里达中央大学）；University of Louisiana at Lafayette（路易斯安那州立大学拉斐特分校）；New York University（纽约大学）；NYUAD Center for Artificial Intelligence and Robotics（纽约大学阿布扎克人工智能与机器人中心）
FairGen: Preference-Aligned Diffusion for Demographically Equitable Medical Image Synthesis
University of Pittsburgh, Swanson School of Engineering（匹兹堡大学斯旺森工程学院）；The University of North Carolina at Chapel Hill, Department of Computer Science（北卡罗来纳大学教堂山分校计算机科学系）；Arizona State University, School of Computing and Augmented Intelligence（亚利桑那州立大学计算与增强智能学院）；University of Pittsburgh, Department of Psychiatry（匹兹堡大学精神病学系）
CompDiff enables fair and zero shot medical image generation across demographic intersections through compositional diffusion
Institute of Data Science, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands（数据科学研究所，科学与工程学院，马斯特里赫特大学，马斯特里赫特，荷兰）；Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands（先进计算科学系，科学与工程学院，马斯特里赫特大学，马斯特里赫特，荷兰）；VITO, Belgium（比利时VITO研究院）
Fairness in Multi-modal Medical Diagnosis with Demonstration Selection
Arizona State University（亚利桑那州立大学）；University of Rochester（罗切斯特大学）；University of Virginia（弗吉尼亚大学）；UCL（伦敦大学学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Central Florida（佛罗里达中央大学）
De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy
Institute for AI in Medicine (IKIM)（人工智能医学研究所（IKIM））；University Hospital Essen（埃森大学医院）；Cancer Research Center Cologne Essen (CCCE)（科隆-埃森癌症研究中心（CCCE））；University Medicine Essen（埃森大学医学部）；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））；Technical University Dortmund（多特蒙德工业大学）
Radiology Report Generation Using Transformers Conditioned with Non-imaging Data
Centre for Computational Imaging and Simulation Technologies in Biomedicine（生物医学计算成像与模拟技术中心）；Leeds Institute for Cardiovascular and Metabolic Medicine（利兹心血管与代谢医学研究所）；School of Medicine（医学院）；School of Computing, University of Leeds（利兹大学计算机学院）
本文提出一种多模态 Transformer 网络，整合胸部 X 射线图像与患者人口统计信息生成放射报告，实验证明加入非影像数据有效提升了生成质量。
The Ohio State University（俄亥俄州立大学）；The Abigail Wexner Research Institute at Nationwide Children’s Hospital（全国儿童医院阿比盖尔·韦克斯纳研究所）
Trauma mortality results from a multitude of non-linear dependent risk factors including patient demographics, injury characteristics, medical care provided, and characteristics of medical facilities; yet traditional approach attempted to capture these relationships using rigid regression models. We hypothesized that a transfer learning based machine learning algorithm could deeply understand a trauma patient's condition and accurately identify individuals at high risk for mortality without relying on restrictive regression model criteria. Anonymous patient visit data were obtained from years …
Are demographically invariant models and representations in medical imaging fair?
Technical University of Denmark（丹麦技术大学）；Pioneer Centre for AI（人工智能先锋中心）；Research Institute for Signals, Systems and Computational Intelligence (CONICET)（信号、系统与计算智能研究所（CONICET））；Universidad Nacional del Litoral（国立 littoral 大学）；University of Copenhagen（哥本哈根大学）；Neurobiology Research Unit, Rigshospitalet（哥本哈根大学医院神经生物学研究单元）
Korea University（高丽大学）；Hallym University（翰林大学）；Dankook University（檀国大学）
It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide groundbreaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with protecting patient's personal information. This paper provides a novel split learning algorithm coined th…
With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly avail…
This paper considers making active learning more sensible from a medical perspective. In practice, a disease manifests itself in different forms across patient cohorts. Existing frameworks have primarily used mathematical constructs to engineer uncertainty or diversity-based methods for selecting the most informative samples. However, such algorithms do not present themselves naturally as usable by the medical community and healthcare providers. Thus, their deployment in clinical settings is very limited, if any. For this purpose, we propose a framework that incorporates clinical insights into…
