Federated learning enables hospitals to collaboratively train segmentation models without sharing patient data. However, current evaluation protocols report only average performance across clients, masking failures at individual sites. In clinical deployment, a model that fails consistently at one hospital is a real safety risk that a good mean score can hide entirely. We introduce MedFL-Stress, a controlled stress-testing framework that exposes exactly this failure mode. Using 2D axial slices from BraTS 2020 distributed across four simulated hospital clients, we apply graded MRI appearance shifts (gamma contrast, scale-shift, and noise-plus-blur) reflecting scanner and acquisition variability in real multi-site deployments. Three federated baselines are evaluated: FedAvg, FedProx, and FedBN. Worst-hospital Dice and inter-hospital disparity are treated as primary metrics, not supplementary observations. FedAvg achieves the highest global mean Dice (0.8159) but conceals a 0.0850 gap between its best and worst-performing hospital. FedBN closes that gap by 41% (0.0850 to 0.0503) while sacrificing less than half a Dice point in mean accuracy (0.8159 to 0.8109), and the weakest hospital gains 3.5 Dice points outright (0.7309 to 0.7656). These findings demonstrate that robustness-oriented evaluation protocols are essential for reliable federated medical imaging deployment.
The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)
Seoul National University（首尔国立大学）；Seoul National University Hospital（首尔国立大学医院）
A meningioma is a type of brain tumor that requires tumor volume size follow ups in order to reach appropriate clinical decisions. A fully automated tool for meningioma detection is necessary for reliable and consistent tumor surveillance. There have been various studies concerning automated lesion detection. Studies on the application of convolutional neural network (CNN)-based methods, which have achieved a state-of-the-art level of performance in various computer vision tasks, have been carried out. However, the applicable diseases are limited, owing to a lack of strongly annotated data bei…
John Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Italian Institute of Technology（意大利技术研究院）；University of California, San Francisco（旧金山大学）；University of California, Berkeley（伯克利大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment
Viterbi School of Engineering（维特比工程学院）；College of Engineering（工程学院）；University of Southern California（南加州大学）；Rochester Institute of Technology（罗切斯特理工学院）；Northeastern University（东北大学）；School of Arts & Sciences（艺术与科学学院）；State University of New York Plattsburgh（纽约州立大学普拉茨堡分校）
Northwood High School（诺斯伍德高中）；Irvine Unified School District（尔湾联合学区）；Fowler School of Engineering（福勒工程学院）；Chapman University（查普曼大学）
Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which including three conventional machine learning methods, support vector machine, random fo…
Lesion segmentation in medical imaging serves as an effective tool for assessing tumor sizes and monitoring changes in growth. However, not only is manual lesion segmentation time-consuming, but it is also expensive and requires expert radiologist knowledge. Therefore many hospitals rely on a loose substitute called response evaluation criteria in solid tumors (RECIST). Although these annotations are far from precise, they are widely used throughout hospitals and are found in their picture archiving and communication systems (PACS). Therefore, these annotations have the potential to serve as a…
RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports
Johns Hopkins University（约翰霍普金斯大学）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（马萨诸塞总医院）；University of California, San Francisco（加利福尼亚大学旧金山分校）；University of Zurich（苏黎世大学）；Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）；Johns Hopkins Medicine（约翰霍普金斯医学）
Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks
SINTEF Digital（SINTEF数字研究所）；Norwegian University of Science and Technology（挪威科技大学）；Amsterdam University Medical Centers（阿姆斯特丹大学医学中心）；Twee Steden Hospital（双城医院）；University College London（伦敦大学学院）
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention
Department of Medical Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS)（医学系，巴基斯坦工程与应用科学研究所）；Pakistan Institute of Engineering and Applied Sciences (PIEAS)（巴基斯坦工程与应用科学研究所）；Department of Physics & Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences (PIEAS)（物理与应用数学系，巴基斯坦工程与应用科学研究所）；Center for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS)（数学科学中心，巴基斯坦工程与应用科学研究所）；Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences（模式识别实验室，计算机与信息科学系，巴基斯坦工程与应用科学研究所）；Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学研究所）；Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences（人工智能中心（PAIC），巴基斯坦工程与应用科学研究所）
Johns Hopkins University（约翰斯·霍普金斯大学）；University Hospital Basel（巴塞尔大学医院）；Stanford University（斯坦福大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Italian Institute of Technology（意大利技术研究院）；University of Bologna（博洛尼亚大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Johns Hopkins Medicine（约翰斯·霍普金斯医疗集团）
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Institute for Systems and Robotics, Instituto Superior Tecnico（高等理工学院系统与机器人研究所）
Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOTA) methods for OS time prediction follow a pre-hoc approach that require computing the segmentation map of the glioma tumor sub-regions (necrotic, edema tumor, enhancing tumor) for estimating OS time. However, the training of the segmentation methods require ground truth segmentation labels which are tedious and expensive to obtain. Given that most of the large-scale data sets available from hospitals are unlikely to…
SINTEF（挪威科技工业研究院）；Bristol Royal Hospital for Children（布里斯托尔皇家儿童医院）；St. Olavs hospital（圣奥拉夫斯医院）
Meningiomas are the most common type of primary brain tumor, accounting for approximately 30% of all brain tumors. A substantial number of these tumors are never surgically removed but rather monitored over time. Automatic and precise meningioma segmentation is therefore beneficial to enable reliable growth estimation and patient-specific treatment planning. In this study, we propose the inclusion of attention mechanisms over a U-Net architecture: (i) Attention-gated U-Net (AGUNet) and (ii) Dual Attention U-Net (DAUNet), using a 3D MRI volume as input. Attention has the potential to leverage t…
Anqing Normal University（安庆师范大学）；Shandong University of Traditional Chinese Medicine（山东中医药大学）；Qingdao Academy of Chinese Medical Sciences（青岛中医科学院）
Background and Objective: Bladder cancer is a common malignant urinary carcinoma, with muscle-invasive and non-muscle-invasive as its two major subtypes. This paper aims to achieve automated bladder cancer invasiveness localization and classification based on MRI. Method: Different from previous efforts that segment bladder wall and tumor, we propose a novel end-to-end multi-scale multi-task spatial feature encoder network (MM-SFENet) for locating and classifying bladder cancer, according to the classification criteria of the spatial relationship between the tumor and bladder wall. First, we b…
Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；Ecole Polytechnique de Montréal（蒙特利尔理工学院）；Tel-Aviv University（特拉维夫大学）；Imperial College London（帝国理工学院）；Hebrew University of Jerusalem（希伯来大学）；Radboud University Medical Center（拉德堡德大学医学中心）；The University of Montréal Hospital Research Centre（蒙特利尔大学医院研究中心）；LMU Munich（慕尼黑大学）
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liv…
Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort
Department of Biomedical Engineering, Linköping University, Sweden（生物医学工程系，利厄普大学）；Center for Medical Image Science and Visualization, Linköping University, Sweden（医学影像科学与可视化中心，利厄普大学）；Crown Princess Victoria Children’s Hospital and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（维多利亚公主儿童医院和健康、医学与关怀科学系，利厄普大学）；Department of Radiology and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（放射科和健康、医学与关怀科学系，利厄普大学）；Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden（肿瘤病理学系，卡罗林斯卡研究所，索纳）；Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Sweden（临床病理学和癌症诊断系，卡罗林斯卡大学医院）；Department of Radiation Physics and Department of Medical and Health Sciences, Linköping University, Sweden（放射物理系和医学与健康科学系，利厄普大学）
A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data
The University of Hong Kong（香港大学）；Xiangya Hospital, Central South University（中南大学湘雅医院）；Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City)（中南大学湘雅医学院常德医院（常德市第一人民医院））；Stanford University（斯坦福大学）；The Second Affiliated Hospital, Hengyang Medical School, University of South China（南华大学衡阳医学院附属第二医院）；The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University（南昌大学江西医学院第二附属医院）；The First Affiliated Hospital, Jiangxi Medical College, Nanchang University（南昌大学江西医学院第一附属医院）；Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College（南昌医学院第一附属医院江西省人民医院）；The Affiliated Children’s Hospital of Xiangya School of Medicine, Hunan Children’s Hospital（湖南儿童医院湘雅医学院附属儿童医院）；Shenzhen Second People’s Hospital（深圳市第二人民医院）；First Hospital of Lanzhou University（兰州大学第一医院）；The Third Xiangya Hospital, Central South University（中南大学湘雅三医院）；Tongji Hospital, School of Medicine, Tongji University（同济大学附属同济医院）；Chongqing Traditional Chinese Medicine Hospital（重庆市中医院）；Basis International School Park Lane Harbour（柏朗思观澜湖国际学校）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University（中南大学湘雅医院国家老年疾病临床医学研究中心）
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
City University of Hong Kong（香港城市大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of California, San Francisco（加州大学旧金山分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Chinese University of Hong Kong（香港中文大学）；NVIDIA（英伟达）
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Cambridge（剑桥大学）；Anhui Provincial Hospital, University of Science and Technology of China（中国科学技术大学附属安徽省立医院）；Nanfang Hospital, Southern Medical University（南方医科大学南方医院）；Sichuan Provincial People’s Hospital（四川省人民医院）；West China Hospital, Sichuan University（四川大学华西医院）；Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China（电子科技大学附属四川省肿瘤医院（研究所））；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center（美国国立卫生研究院临床中心放射与成像科学部成像生物标志物和计算机辅助诊断实验室）；Ping An Insurance Company of China（中国平安保险（集团）股份有限公司）
Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth. This task, however, is very challenging since manual segmentation is prohibitively time-consuming, expensive, and requires professional knowledge. Current practices rely on an imprecise substitute called response evaluation criteria in solid tumors (RECIST). Although these markers lack detailed information about the lesion regions, they are commonly found in hospitals' picture archiving and communication systems (PACS). Thus, these markers have the potential to se…
National Institutes of Health（美国国立卫生研究院）；University of Florida（佛罗里达大学）；Nvidia Corp（英伟达公司）；Ping An Insurance (Group) Company of China Ltd（中国平安保险（集团）股份有限公司）
Volumetric lesion segmentation via medical imaging is a powerful means to precisely assess multiple time-point lesion/tumor changes. Because manual 3D segmentation is prohibitively time consuming and requires radiological experience, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Despite their coarseness, RECIST marks are commonly found in current hospital picture and archiving systems (PACS), meaning they can provide a potentially powerful, yet extraordinarily challenging, source of weak supervision for full 3D segmentation. Towa…
University Hospital Erlangen（埃尔朗根大学医院）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-EMN综合癌症中心）；Pattern Recognition Lab（模式识别实验室）
Stereotactic radiotherapy (SRT) is one of the most important treatment for patients with brain metastases (BM). Conventionally, following SRT patients are monitored by serial imaging and receive salvage treatments in case of significant tumor growth. We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth, enabling personalized follow-up intervals and early selection for salvage treatment. All experiments are performed on a dataset from clinical routine…
Adaptive Local Binary Pattern: A Novel Feature Descriptor for Enhanced Analysis of Kidney Abnormalities in CT Scan Images using ensemble based Machine Learning Approach
German University in Cairo（开罗德国大学）；Ain Shams University（艾因夏姆斯大学）
Brain tumors are one of the life-threatening forms of cancer. Previous studies have classified brain tumors using deep neural networks. In this paper, we perform the later task using a collaborative deep learning technique, more specifically split learning. Split learning allows collaborative learning via neural networks splitting into two (or more) parts, a client-side network and a server-side network. The client-side is trained to a certain layer called the cut layer. Then, the rest of the training is resumed on the server-side network. Vertical distribution, a method for distributing data …
Curia: A Multi-Modal Foundation Model for Radiology
Raidium；Hôpital Européen Georges Pompidou, AP-HP（欧洲乔治·蓬皮杜医院, AP-HP）；Université Paris-Cité（巴黎城市大学）；INRIA（法国国家信息与自动化技术研究所）；INSERM（法国国家卫生与医学研究中心）；Beaujon Hospital（贝琼医院）；Medical University of South Carolina（南卡罗来纳医科大学）；Columbia University Irving Medical Center（哥伦比亚大学伊万斯医学中心）；Centre Cardiologique du Nord（北心脑血管中心）
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute（矢量研究所）
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While there has been extensive research focused on optimizing brain tumor segmentation in the adult population, studies on AI guided pediatric tumor segmentation are scarce. Furthermore, MRI signal characteristics of pediatric and adult brain tumors differ, necessitating the development of segmentation algorithms specifically designed for pediatric brain tumors. We developed a segmentation model trained on magnetic resonance…
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology
Technical University Dresden（德累斯顿工业大学）；University of St Andrews（圣安德鲁斯大学）；Helmholtz Munich – German Research Center for Environment and Health（亥姆霍兹慕尼黑中心-德国环境与健康研究中心）；Technical University of Munich（慕尼黑工业大学）；University Hospital Aachen（亚琛大学医院）；University Medical Center Mainz（美因茨大学医学中心）；University of Leeds（利兹大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心 (NCT)）；University Hospital Heidelberg（海德堡大学医院）；Technische Universität Dresden（德累斯顿工业大学）
SINTEF（挪威科技工业研究院）；Bristol Royal Hospital for Children（布里斯托尔皇家儿童医院）；St. Olavs hospital（圣奥拉夫医院）
Automatic and consistent meningioma segmentation in T1-weighted MRI volumes and corresponding volumetric assessment is of use for diagnosis, treatment planning, and tumor growth evaluation. In this paper, we optimized the segmentation and processing speed performances using a large number of both surgically treated meningiomas and untreated meningiomas followed at the outpatient clinic. We studied two different 3D neural network architectures: (i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight multi-scale architecture (PLS-Net). In addition, we studied the impact of dif…
Tlemcen University（特莱姆森大学）；Université de Bourgogne（勃艮第大学）
During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer. In this work, we present an automated diagnosis system based on the ABCD rule used in clinical diagnosis in order to discriminate benign from malignant skin lesions. First, to reduce the influence of small structures, a preprocessing step based on morphological and fast marching schemes is used. In the second step, an unsupervised approach for lesion se…
Colorectal Cancer Histopathological Grading using Multi-Scale Federated Learning
Department of Computer Science and Engineering, European University of Bangladesh（欧洲 bangladesh 大学 计算机科学与工程系）；Department of Computer Science and Engineering , BRAC University,Dhaka, Bangladesh（brac 大学 计算机科学与工程系）；Department of Computer Science and Engineering , Daffodil International University, Dhaka, Bangladesh（daffodil 国际大学 计算机科学与工程系）；Department of Institute of Information Technology, University of Dhaka, Bangladesh（达卡大学 信息科技学院）；Department of Computer Science and Engineering , BRAC University,Dhaka（brac 大学 计算机科学与工程系）；Ochsner Center for Outcomes Research, Ochsner Research, New Orleans, LA 70121, USA（ochsner 成果研究中心，ochsner 研究）
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
University of Zurich（苏黎世大学）；Technical University Munich（慕尼黑工业大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；Mayo Clinic（梅奥诊所）；Helmholtz AI, Helmholtz Munich（亥姆霍兹慕尼黑亥姆霍兹人工智能研究所）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学转化肿瘤研究中心TranslaTUM）；Children’s National Hospital（全国儿童医院）；George Washington University（乔治华盛顿大学）；Finnish Center for Artificial Intelligence（芬兰人工智能中心）；Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School（哈佛医学院阿西努拉·A·马蒂诺斯生物医学成像中心）；Crestview Radiology（克雷斯特维尤放射科）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Yale University（耶鲁大学）；Children’s Hospital of Philadelphia, University of Pennsylvania（宾夕法尼亚大学费城儿童医院）；Duke University Medical Center（杜克大学医学中心）
National Institutes of Health（美国国立卫生研究院）；University of Florida（佛罗里达大学）；Ping An Insurance (Group) Company of China, Ltd.（中国平安保险（集团）股份有限公司）
Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes. However, because manual 3D segmentation is prohibitively time consuming, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Despite their coarseness, RECIST markers are commonly found in current hospital picture and archiving systems (PACS), meaning they can provide a potentially powerful, yet extraordinarily challenging, source of weak supervision for full 3D segmentation. Toward this e…
Mix-modal Federated Learning for MRI Image Segmentation
School of Artificial Intelligence（人工智能学院）；School of Computer Science and Technology（计算机科学与技术学院）；State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology（光电信息采集与防护技术国家重点实验室）；Anhui Provincial Key Laboratory of Security Artificial Intelligence（安徽省安全人工智能重点实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）
Tecnologico de Monterrey（蒙特雷科技大学）；CIATEQ A.C.（CIATEQ先进技术中心）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Hospital Sant Joan de Déu（圣胡安德迪奥斯医院）
Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the diagnosis of prostate cancer is the localization of Regions of Interest (ROI) containing a tumor tissue. Currently, the segmentation of this ROI in most cases is carried out manually by expert doctors, but the procedure is plagued with low detection rates (of about 27-44%) or overdiagnosis in some patients. Therefore, several research works have tackled the challenge of automatically segmenting and extracting features o…
