Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health（亥姆霍兹慕尼黑中心-德国环境健康研究中心计算生物学研究所）；Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；Helmholtz AI, Helmholtz Center Munich（慕尼黑亥姆霍兹中心亥姆霍兹人工智能）；Red Blood Cell Research Group, Institute of Veterinary Physiology, Vetsuisse Faculty and the Zurich Center for Integrative Human Physiology, University of Zurich（苏黎世大学兽医学院兽医生理学研究所红细胞研究组及苏黎世综合人类生理学中心）；Computer Aided Medical Procedures, Johns Hopkins University（约翰斯·霍普金斯大学计算机辅助医疗程序研究所）；Computer Vision Lab (CVL), ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）
Ario Sadafi, Asya Makhro, Anna Bogdanova, Nassir Navab, Tingying Peng, Shadi Albarqouni, Carsten Marr
Red blood cells are highly deformable and present in various shapes. In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis. However, manually labeling of all cells is laborious, complicated and introduces inter-expert variability. We propose an attention based multiple instance learning method to classify blood samples of patients suffering from blood cell disorders. Cells are detected using an R-CNN architecture. With the features extracted for each cell, a multiple instance learning method classifies patient samples into one out of four blood cell disorders. The attention mechanism provides a measure of the contribution of each cell to the overall classification and significantly improves the network's classification accuracy as well as its interpretability for the medical expert.
Anomaly-aware multiple instance learning for rare anemia disorder classification
Helmholtz Munich – German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心）；Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；University Hospital Bonn（波恩大学医院）
STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis
Laboratory of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University（数字图像与智能计算实验室，信息工程学院，上海 Maritime 大学）；Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University（中英双语研究部，香港理工大学）；Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University（神经科，徐州医学院附属连云港医院）
针对 fMRI 脑疾病分类忽略时空依赖的问题，提出 STARFormer，通过 ROI 空间重组、时间特征重组和时空融合三个模块，在 ASD 和 ADHD 数据集上达到最先进性能。
Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images
Institute of AI for Health, Helmholtz Munich – German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心健康人工智能研究所）；Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
A COCO-Formatted Instance-Level Dataset for Plasmodium Falciparum Detection in Giemsa-Stained Blood Smears
MIRA Vision Microscopy GmbH（MIRA视觉显微镜 GmbH）；Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg（模式识别实验室，计算机科学系，弗赖堡-艾尔兰根-纽伦堡大学）
Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；The Third Affiliated Hospital of Sun Yat-sen University（中山大学附属第三医院）；School of Computer Science, South China Normal University（华南师范大学计算机学院）
Robust White Blood Cell Classification with Stain-Normalized Decoupled Learning and Ensembling
University of Technology, Ho Chi Minh City, Vietnam（越南胡志明市技术大学）；University of Science, Ho Chi Minh City, Vietnam（越南胡志明市科学大学）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学）；Carnegie Mellon University, Pittsburgh, PA, USA（卡内基梅隆大学）；Northwestern University, Chicago, IL, USA（西北大学）
WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images
Rapid-Rich Object Search (ROSE) Lab, School of Electrical and Electronic Engineering, Nanyang Technological University（快速丰富目标搜索（ROSE）实验室，电气与电子工程学院，南洋理工大学）；Shanghai University of Finance and Economics（上海财经大学）
Deep Generative Classification of Blood Cell Morphology
University of Cambridge（剑桥大学）；UZ Leuven（鲁汶大学医院）；University of Leuven（鲁汶大学）；KU Leuven（鲁汶大学）；Zuyderland Medical Center（宰德兰医学中心）；Barts Health NHS Trust（巴茨健康国民保健服务信托）；University College London（伦敦大学学院）；Queen Mary University of London（伦敦玛丽女王大学）
Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks
Helmholtz Zentrum München - German Research Center for Environmental Health（慕尼黑亥姆霍兹中心 - 德国环境健康研究中心）；Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias
Helmholtz Center Munich, German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
Helmholtz Munich（亥姆霍兹慕尼黑中心）；Ludwig-Maximilians-University Munich（慕尼黑大学）；Dr. von Haunersches Kinderspital（冯·豪内尔施儿童医院）；King’s College London（伦敦国王学院）；Technical University of Munich（慕尼黑工业大学）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）
MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
PolyU；South China University of Technology（南方科技大学）；Tsinghua University（清华大学）；YMSC, Tsinghua University（清华大学交叉信息学院）；Dept. of Biomedical Engineering, PolyU（PolyU 生物医学工程系）；Sch. of Future Technology, South China University of Technology（南方科技大学未来技术学院）；Dept. of Health Technology and Informatics, PolyU（PolyU 健康技术与信息学系）；Dept. of Biomedical Engineering and the Dept. of Data Science and Artificial Intelligence, PolyU（PolyU 生物医学工程系和数据科学与人工智能系）
