Toward explainable AI approaches for breast imaging: adapting foundation models to diverse populations
University of Pennsylvania（宾夕法尼亚大学）；Federal University of Paraíba（帕拉州联邦大学）；University of Bristol（布里斯托大学）
Guilherme J. Cavalcante, José Gabriel A. Moreira, Gabriel A. B. do Nascimento, Vincent Dong, Alex Nguyen, Thaís G. do Rêgo, Yuri Malheiros, Telmo M. Silva Filho… 展开作者
Guilherme J. Cavalcante, José Gabriel A. Moreira, Gabriel A. B. do Nascimento, Vincent Dong, Alex Nguyen, Thaís G. do Rêgo, Yuri Malheiros, Telmo M. Silva Filho, Carla R. Zeballos Torrez, James C. Gee, Anne Marie McCarthy, Andrew D. A. Maidment, Bruno Barufaldi
Foundation models hold promise for specialized medical imaging tasks, though their effectiveness in breast imaging remains underexplored. This study leverages BiomedCLIP as a foundation model to address challenges in model generalization. BiomedCLIP was adapted for automated BI-RADS breast density classification using multi-modality mammographic data (synthesized 2D images, digital mammography, and digital breast tomosynthesis). Using 96,995 images, we compared single-modality (s2D only) and multi-modality training approaches, addressing class imbalance through weighted contrastive learning. Both approaches achieved similar accuracy (multi-modality: 0.74, single-modality: 0.73), with the multi-modality model offering broader applicability across different imaging modalities and higher AUC values consistently above 0.84 across BI-RADS categories. External validation on the RSNA and EMBED datasets showed strong generalization capabilities (AUC range: 0.80-0.93). GradCAM visualizations confirmed consistent and clinically relevant attention patterns, highlighting the models interpretability and robustness. This research underscores the potential of foundation models for breast imaging applications, paving the way for future extensions for diagnostic tasks.
Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI
Southern Illinois University Carbondale（南伊利诺伊大学卡本代尔分校）；Daffodil International University（达夫迪尔国际大学）；King Saud University（沙特国王大学）；Bahir Dar University（巴赫达尔大学）；Laval University（拉瓦尔大学）；Ibn Zohr University（伊本·佐赫尔大学）
Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques
Canadian Institute of Cybersecurity (CIC)（加拿大网络安全研究所）；University of New Brunswick（新不伦瑞克大学）；Dipartimento di Informatica, Università di Verona（威尼斯大学信息学院）；Università di Verona（威尼斯大学）；Department of Computer Science, School of Mathematics and Computer Science, Institute of Business Administration（计算机科学系，数学与计算机科学学院，商学院）；LIRIS, CNRS, Université Claude Bernard Lyon 1（LIRIS，国家科学研究中心，克莱尔伯恩大学 Lyon 1 分校）；LHC, UMR5516, Université de Saint-Etienne（LHC，UMR5516，圣艾蒂安大学）
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Evolution Online LLC（Evolution Online有限责任公司）
We investigated the data-driven relationship between features in the tumor microenvironment (TME) and the overall and 5-year survival in triple-negative breast cancer (TNBC) and non-TNBC (NTNBC) patients by using Explainable Artificial Intelligence (XAI) models. We used clinical information from patients with invasive breast carcinoma from The Cancer Genome Atlas and from two studies from the cbioPortal, the PanCanAtlas project and the GDAC Firehose study. In this study, we used a normalized RNA sequencing data-driven cohort from 1,015 breast cancer patients, alive or deceased, from the UCSC X…
Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework
Morgan State University（摩根州立大学）；Wrexham University（雷克瑟姆大学）；Federal University of Technology, Minna（明纳联邦理工大学）；Federal University of Education, Kano（卡诺联邦教育大学）
PSO-XAI: A PSO-Enhanced Explainable AI Framework for Reliable Breast Cancer Detection
Department of Information and Communication Engineering, Noakhali Science and Technology University（信息与通信工程系，诺阿克利科学与技术大学）；Department of Computer Science & Engineering, Netrokona University（计算机科学与工程系，纳特罗克纳大学）；Department of Computer and Communication Engineering, International Islamic University Chittagong（计算机与通信工程系，国际伊斯兰大学查塔格ONG）；Department of Mechatronics and Industrial Engineering, Chittagong University of Engineering and Technology（机电与工业工程系，查塔格ONG工程与技术大学）；School of Computing Sciences and Computer Engineering, University of Southern Mississippi（计算科学与计算机工程学院，密西西比州立大学）；AI & Digital Health Technology, Artificial Intelligence and Cyber Futures Institute, Charles Sturt University（人工智能与数字健康技术，人工智能与网络未来研究院，查尔斯·斯图尔特大学）；AI & Digital Health Technology, Rural Health Research Institute, Charles Sturt University（人工智能与数字健康技术，农村健康研究学院，查尔斯·斯图尔特大学）
National Institute of Standards and Technology（美国国家标准与技术研究院）
Traditionally, researchers in automatic face recognition and biometric technologies have focused on developing accurate algorithms. With this technology being integrated into operational systems, engineers and scientists are being asked, do these systems meet societal norms? The origin of this line of inquiry is `trust' of artificial intelligence (AI) systems. In this paper, we concentrate on adapting explainable AI to face recognition and biometrics, and we present four principles of explainable AI to face recognition and biometrics. The principles are illustrated by $\it{four}$ case studies,…
Bridging Accuracy and Interpretability: Deep Learning with XAI for Breast Cancer Detection
Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur - 208016, Uttar Pradesh, India（数学与统计学系，印度理工学院坎浦尔学院，坎浦尔 - 208016，印度乌塔兰普尔州）
A Lightweight CNN Integrated Compact Convolutional Transformer for Multi-Scale Feature Learning and reducing computational complexity for breast cancer mammography image detection and classification
Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction
AI Technology for Life（人工智能技术与生命科学）；Department of Information and Computing Sciences（信息与计算科学系）；Department of Biology（生物学系）；Utrecht University（乌得勒支大学）；Department of Metabolic Diseases（代谢疾病部门）；Wilhelmina Children’s Hospital（维廉明娜儿童医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Regenerative Medicine Center Utrecht（乌得勒支再生医学中心）；Computational Pathology（计算病理学）；Department of Pathology（病理学系）；The Netherlands Cancer Institute（荷兰癌症研究所）；Visualization and Graphics（可视化与图形学）；The Netherlands Cancer Institute, Amsterdam（荷兰癌症研究所，阿姆斯特丹）
EPFL（洛桑联邦理工学院）；IBM Research Zürich（IBM苏黎世研究院）；ETH Zürich（苏黎世联邦理工学院）；National Cancer Institute - IRCCS-Fondazione Pascale（国家癌症研究所 - IRCCS帕斯卡尔基金会）
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics. Recently, graph techniques encoding relevant biological entities have been employed to represent and assess DP images. Such paradigm shift from pixel-wise to entity-wise analysis provides more control over concept representation. In this paper, we introduce a post-hoc explainer to derive compact per-instance explanations emphasizing diagnostically important entities in the graph. Although we focus our analyses to cells and cellular interactions in …
GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology
School of Computer Science and Engineering（计算机科学与工程学院）；University of New South Wales（新南威尔士大学）；St. George and Sutherland Clinical School（圣乔治与萨瑟兰临床学院）；Department of Anatomical Pathology（解剖病理学部）；NSW Health Pathology（新南威尔士州卫生病理学）；Cancer Care Centre（癌症护理中心）
LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling
Netherlands Cancer Institute（荷兰癌症研究所）；Radboud University Medical Center（拉德堡德大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Macao Polytechnic University（澳门理工大学）
BACON: A fully explainable AI model with graded logic for decision making problems
Department of Information Systems University of Maryland, Baltimore County (UMBC)（信息系统系大学马里兰大学巴尔的摩县（UMBC））；Department of Computer Science San Francisco State University（计算机科学系旧金山州立大学）；Department of Information Systems UMBC（信息系统系大学马里兰大学巴尔的摩县（UMBC））
In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians. This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical pra…
University of Minnesota（明尼苏达大学）；University of Cincinnati（辛辛那提大学）
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis base…
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School（布伦瑞克工业大学与汉诺威医学院彼得·L·赖歇茨医学信息学研究所）；Hannover Medical School（汉诺威医学院）
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives
National Technical University of Athens（雅典国立技术大学）；School of Electrical and Computer Engineering（电气与计算机工程学院）；Artificial Intelligence and Learning Systems Laboratory（人工智能与学习系统实验室）
GAS-MIL: Group-Aggregative Selection Multi-Instance Learning for Ensemble of Foundation Models in Digital Pathology Image Analysis
The University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；Simmons Comprehensive Cancer Center（西蒙斯综合癌症中心）
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
University Hospital Aachen（亚琛大学医院）；Technical University Dresden（德累斯顿工业大学）；University Hospital Dresden（德累斯顿大学医院）；University Hospital Heidelberg（海德堡大学医院）
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity
Kırıkkale University（克尔克卡拉大学）；University College Dublin（都柏林大学学院）；CeADAR；School of Computer Science（计算机科学学院）
An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification
Department of Health Outcomes and Biomedical Informatics, University of Florida（健康结果与生物医学信息学系，佛罗里达大学）；Tiktok；Department of Biostatistics, MD Anderson Cancer Center（生物统计学系，MD安德森癌症中心）；Department of Urology, University of Florida（泌尿科，佛罗里达大学）
Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis
National Cheng Kung University（成功大学）；Saigon Precision Medicine Research Center (SAIGONMEC)（西贡精准医学研究中心）；Taipei Medical University（台北医学大学）；Ho Chi Minh City International University（胡志明市国际大学）；Chonnam National University（全南国立大学）；Vietnamese-German University（越德大学）；Research Center for Artificial Intelligence in Medicine, Taipei Medical University（台北医学大学医学人工智能研究中心）
Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
Department of Health Technology and Informatics, The Hong Kong Polytechnic University（健康科技与信息学系，香港理工大学）；Centre for Smart Health and School of Nursing, The Hong Kong Polytechnic University（智能健康中心及护理学院，香港理工大学）；Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong（影像与介入放射学系，香港中文大学）；Suzhou Hospital of Traditional Chinese Medicine Affiliated to Nanjing University of Chinese Medicine（南京中医药大学附属苏州中医医院）；Department of Ultrasound, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University（南京医科大学附属常州第二人民医院超声科）
PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation
Department of Computer Science, Quaid-e-Awam University of Engineering, Sciences & Technology（计算机科学系，卡迪尔-阿瓦姆工程、科学与技术大学）；Department of Artificial Intelligence, Quaid-e-Awam University of Engineering, Sciences & Technology（人工智能系，卡迪尔-阿瓦姆工程、科学与技术大学）；Department of Computer Science, Sindh Madressatul Islam University, City Campus, Karachi（计算机科学系， Sind 阿里斯坦伊斯兰大学，卡拉奇城校区）；Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London（计算机科学与数字技术系，建筑、计算与工程学院，东伦敦大学）
