Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights into cell-type heterogeneity and spatial organization. However, current computational pipelines rely on cell segmentation algorithms, which require laborious fine-tuning and can introduce downstream errors due to inaccurate single-cell representations. We propose a segmentation-free deep learning approach that leverages grouped convolutions to learn interpretable embedded features from each imaging channel, enabling robust cell-type identification without manual feature selection. Validated on an Imaging Mass Cytometry dataset of 1.8 million cells from neuroblastoma patients, our method enables the accurate identification of known cell types, showcasing its scalability and suitability for high-dimensional MI data.
Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification
Harvard Medical School and Massachusetts General Hospital（哈佛医学院和麻省总医院）；Graduate Institute of Biomedical Electronics and Bioinformatics（生物医学电子与生物信息学研究生院）；University of Southern California（南加州大学）
Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images
Dept. of Information Technology（信息技术系）；Dept. of Molecular, Cell and Developmental Biology（分子、细胞与发育生物学系）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Disentangling Prompt Dependence to Evaluate Segmentation Reliability in Gynecological MRI
Laboratoire Traitement du Signal et de l’Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France（信号与图像处理实验室（LTSI，INSERM UMR 1099），雷恩大学，法国雷恩）；Centre Hospitalier Universitaire de Rennes (CHU Rennes), Rennes, France（雷恩大学医院（CHU Rennes），法国雷恩）
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Imperial College London（帝国理工学院）；MRC London Clinical Sciences Centre（MRC伦敦临床科学中心）；National Heart & Lung Institute（国家心肺研究所）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学研究生医学院）
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）
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays
Hasso Plattner Institute, University of Potsdam（波茨坦大学哈索·普拉特纳研究所）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Contraste Radiologia Odontológica（康特拉斯特牙科放射诊所）；Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山哈索·普拉特纳数字健康研究所）
Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits
Weill Cornell Medicine（威尔康奈尔医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；University of Pisa（比萨大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Massachusetts Institute of Technology（麻省理工学院）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Tokyo University of Agriculture and Technology (TUAT)（东京农工大学）；RIKEN Center for Advanced Intelligence Project (RIKEN AIP)（理化学研究所先进智能研究中心）；Graduate School of Medicine, Juntendo University（顺天堂大学医学院）；Faculty of Health Data Science, Juntendo University（顺天堂大学健康数据科学学部）；RIKEN（理化学研究所）
ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation
University Hospital Bonn（波恩大学医院）；LMU University Hospital（慕尼黑莱茵河医学院医院）；University Hospital Düsseldorf（杜伊斯堡大学医院）；Heidelberg University Hospital（海德堡大学医院）；Università Cattolica del Sacro Cuore（罗马天主教圣心大学）；University Hospital Zurich（苏黎世大学医院）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；DTU Compute（技术大学计算学院）；Helmholtz AI（海德堡中心人工智能）
Improved post-hoc probability calibration for out-of-domain MRI segmentation
Imperial College London（帝国理工学院）；Fudan University（复旦大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Technical University of Munich（慕尼黑工业大学）
Multi-Modal Evaluation Approach for Medical Image Segmentation
LIPN-UMR-CNRS 7030（法国国家科学研究中心7030联合研究单元LIPN实验室）；Sorbonne University Paris Nord（巴黎北索邦大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Laboratory of Images, Signals and Intelligent Systems（图像、信号与智能系统实验室）；Université Paris-Est Créteil（巴黎东克雷泰伊大学）
Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation
Manchester Metropolitan University（曼彻斯特城市大学）；Lancashire Teaching Hospitals NHS Foundation Trust（兰开夏教学医院NHS基金会信托）；Waikato District Health Board（怀卡托地区卫生委员会）；United Lincolnshire Hospitals NHS Trust（联合林肯郡医院NHS信托）；Jersey General Hospital（泽西总医院）；Eastbourne District General Hospital（伊斯特本地区综合医院）
SegCompass: Exploring Interpretable Alignment with Sparse Autoencoders for Enhanced Reasoning Segmentation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Peng Cheng Laboratory（鹏城实验室）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Meituan, Beijing（美团，北京）；University of Chinese Academy of Sciences（中国科学院大学）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）
Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK（应用数学与理论物理系，剑桥大学）；Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, USA（计算与数据科学学院，伊利诺伊大学厄巴纳-香槟分校）；Zurich University of Applied Sciences, Switzerland（苏黎世应用科学大学）；Lucerne Cantonal Hospital, Switzerland（卢塞恩州立医院）；Lucerne University of Applied Sciences and Arts, Switzerland（卢塞恩应用科学与艺术大学）；Yau Mathematical Sciences Center, Tsinghua University, China（尤太数学科学中心，清华大学）
Semantic segmentation of multispectral photoacoustic images using deep learning
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Heidelberg University（海德堡大学）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；Cancer Research UK Cambridge Institute（英国癌症研究院剑桥研究所）；University of Cambridge（剑桥大学）
