High resolution micro-ultrasound has demonstrated promise in real-time prostate cancer detection, with deep learning becoming a prominent tool for learning complex tissue properties reflected on ultrasound. However, a significant roadblock to real-world deployment remains, which prior works often overlook: model performance suffers when applied to data from different clinical centers due to variations in data distribution. This distribution shift significantly impacts the model's robustness, posing major challenge to clinical deployment. Domain adaptation and specifically its test-time adaption (TTA) variant offer a promising solution to address this challenge. In a setting designed to reflect real-world conditions, we compare existing methods to state-of-the-art TTA approaches adopted for cancer detection, demonstrating the lack of robustness to distribution shifts in the former. We then propose Diverse Ensemble Entropy Minimization (DEnEM), questioning the effectiveness of current TTA methods on ultrasound data. We show that these methods, although outperforming baselines, are suboptimal due to relying on neural networks output probabilities, which could be uncalibrated, or relying on data augmentation, which is not straightforward to define on ultrasound data. Our results show a significant improvement of $5\%$ to $7\%$ in AUROC over the existing methods and $3\%$ to $5\%$ over TTA methods, demonstrating the advantage of DEnEM in addressing distribution shift.
\keywords{Ultrasound Imaging \and Prostate Cancer \and Computer-aided Diagnosis \and Distribution Shift Robustness \and Test-time Adaptation.}
Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data based on Convolutional Neural Networks
Institut für Medizinische Informatik, Universität zu Lübeck（吕贝克大学医学信息学研究所）；FUSE-AI GmbH, Hamburg（汉堡FUSE-AI有限公司）；Cantonal Hospital Aarau, Aarau (CH)（阿劳州立医院）
Segmentation Strategies in Deep Learning for Prostate Cancer Diagnosis: A Comparative Study of Mamba, SAM, and YOLO
School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院）；School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；School of Electrical Engineering, Shahrood Branch, Islamic Azad University（伊斯兰阿扎德大学沙赫鲁德分校电气工程学院）；Medical Sciences and Technologies Department, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学科学研究分支医学与技术系）；Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院生物医学工程系）
PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA（计算病理学部，病理与实验室医学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）；Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA（印第安纳大学Melvin和Bren Simon综合癌症中心，印第安纳波利斯，印第安纳州，美国）；Luddy School of Informatics, Computing, and Engineering, Indiana University, IN, USA（卢迪信息学、计算与工程学院，印第安纳大学，印第安纳州，美国）；Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA（放射学与成像科学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）；Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA（生物统计学与健康数据科学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）
Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry
Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；Karolinska Institutet（卡罗林斯卡学院）；Synlab；University of Bergen（卑尔根大学）；Griffith University（格里菲斯大学）；SciLifeLab
Extracting and analyzing 3D histomorphometric features related to perineural and lymphovascular invasion in prostate cancer
Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；University of Chicago（芝加哥大学）；University of California, Berkeley（加州大学伯克利分校）；Public Health Agency of Catalonia（加泰罗尼亚公共卫生局）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Atlanta VA Medical Center（亚特兰大退伍军人事务医疗中心）；University of Pennsylvania（宾夕法尼亚大学）
Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging
UCL Hawkes Institute and the Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院霍克斯研究所与医学物理与生物医学工程系）；Centre for Bioengineering, School of Engineering and Materials Science and Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院生物工程中心与数字环境研究所）；Centre for Medical Imaging, University College London（伦敦大学学院医学影像中心）；Division of Surgery & Interventional Science, University College London（伦敦大学学院外科与介入科学部）；Urological Research Network（泌尿研究网络）
Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning
UCL Hawkes Institute \& Department of Medical Physics；Biomedical Engineering, University College London, United Kingdom Aga Khan University Hospital, Pakistan Centre for Bioengineering \& School of Engineering；Materials Science \& Digital Environment Research Institute, Queen Mary University of London, United Kingdom
Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection
University of Stavanger（斯塔万格大学）；Stavanger University Hospital（斯塔万格大学医院）；Karolinska Institutet（卡罗林斯卡学院）；Capio S:t Gorän Hospital（卡皮奥圣戈兰医院）；Danderyd Hospital（丹德里德医院）
Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection
National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心）；Center for Cancer Research, National Cancer Institute（美国国家癌症研究所癌症研究中心）
Towards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound
School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University（深圳大学医学部生物医学工程学院）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）
Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound
Stanford University（斯坦福大学）；The Catholic University of Korea（韩国天主教大学）；University of Alberta（阿尔伯塔大学）；University of California Los Angeles（加州大学洛杉矶分校）；Humanitas University（乌尼塔斯大学）；IRCCS Humanitas Research Hospital（IRCCS乌尼塔斯研究医院）；University of Florida（佛罗里达大学）；University of California San Diego（加州大学圣地亚哥分校）
