Differentiating the intrinsic subtypes of breast cancer is crucial for deciding the best treatment strategy. Deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods, but to date, deep learning has not been directly utilized to examine which genes are associated with which subtypes. To clarify the mechanisms embedded in the intrinsic subtypes, we developed an explainable deep learning model called a point-wise linear (PWL) model that generates a custom-made logistic regression for each patient. Logistic regression, which is familiar to both physicians and medical informatics researchers, allows us to analyze the importance of the feature variables, and the PWL model harnesses these practical abilities of logistic regression. In this study, we show that analyzing breast cancer subtypes is clinically beneficial for patients and one of the best ways to validate the capability of the PWL model. First, we trained the PWL model with RNA-seq data to predict PAM50 intrinsic subtypes and applied it to the 41/50 genes of PAM50 through the subtype prediction task. Second, we developed a deep enrichment analysis method to reveal the relationships between the PAM50 subtypes and the copy numbers of breast cancer. Our findings showed that the PWL model utilized genes relevant to the cell cycle-related pathways. These preliminary successes in breast cancer subtype analysis demonstrate the potential of our analysis strategy to clarify the mechanisms underlying breast cancer and improve overall clinical outcomes.
One-dimensional convolutional neural network model for breast cancer subtypes classification and biochemical content evaluation using micro-FTIR hyperspectral images
Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院）；Radiopharmacy Center, Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院放射药学中心）
Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67
Energy and Nuclear Research Institute, University of São Paulo（圣保罗大学能源与核研究学院）；University of São Paulo（圣保罗大学）
Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images
Uit The Arctic University of Norway（挪威北极大学）；University Hospital of North Norway（北挪威大学医院）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Oslo University Hospital（奥斯陆大学医院）
Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging
Università di Genova（热那亚大学）；Universitat de Barcelona（巴塞罗那大学）；IRCCS Ospedale Policlinico San Martino（圣马蒂诺国家研究与护理综合医院）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高等研究院）；Universidad Autónoma de Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）
A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection
Austrian Center for Medical Innovation and Technology（奥地利医学创新与技术中心）；Danube Private University（多瑙河私立大学）；Medical University of Vienna（维也纳医科大学）；Patho im Zentrum（中心病理机构）；Institute for Systems and Robotics (ISR-Lisboa/LARSyS)（系统与机器人研究所（里斯本系统与机器人研究所/卢西塔尼亚机器人与系统协会））；Instituto Superior Técnico（高等技术学院）；University of Lisbon（里斯本大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
Contrasting Low and High-Resolution Features for HER2 Scoring using Deep Learning
Centre for Visual Information Technology（视觉信息技术中心）；Center for Computational Natural Sciences and Bioinformatics（计算自然科学与生物信息学中心）；International Institute of Information Technology（国际信息技术学院）
Beyond Labels: A Self-Supervised Framework with Masked Autoencoders and Random Cropping for Breast Cancer Subtype Classification
DISIT, Computer Science Institute, University of Piemonte Orientale（DISIT计算机科学研究所，皮埃蒙特东方大学）；Center for Translational Research on Autoimmune and Allergic Diseases (CAAD), Department of Health Sciences, University of Piemonte Orientale（自身免疫和过敏疾病转化研究中心（CAAD），健康科学系，皮埃蒙特东方大学）；Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Department of Health Sciences, University of Piemonte Orientale（自身免疫疾病跨学科研究中心（IRCAD），健康科学系，皮埃蒙特东方大学）
提出一种基于 MAE 与随机裁剪的自监督框架，从 WSI 学习组织病理图像表示，并通过线性探针完成乳腺癌多亚型分类。
Multi-modal AI for comprehensive breast cancer prognostication
Ataraxis AI（阿塔拉克西斯人工智能公司）；Karmanos Cancer Institute（卡马诺斯癌症研究所）；Cancer Center Baselland（巴塞尔兰癌症中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；The Catholic University of Korea（韩国天主教大学）；UChicago Medicine（芝加哥大学医学中心）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；NYU Center for Data Science（纽约大学数据科学中心）；NYU Courant Institute of Mathematical Sciences（纽约大学柯朗数学科学研究所）；The University of Aberdeen（阿伯丁大学）；University Hospital Basel（巴塞尔大学医院）；Cancer Research Malaysia（马来西亚癌症研究院）；Vilnius University（维尔纽斯大学）；Providence Health（普罗维登斯医疗保健机构）
CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
College of Computer Science, Sichuan University（四川大学计算机学院）；Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences（中国科学院新疆物理化学研究所）；College of Mathematics, Sichuan University（四川大学数学学院）；School of Artificial Intelligence and Data Science, University of International Business and Economics（国际经贸大学人工智能与数据科学学院）
Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data
National Research Council of Italy (CNR), Institute of Informatics（意大利国家研究理事会（CNR）信息学院）；Computational Biomedicine Unit, Department of Medical Sciences, University of Turin（都灵大学医学科学系计算生物医学单元）；University of Siena（锡耶纳大学）；Institute for High Performance Computing（高性能计算研究所）；Networking (ICAR), National Research Council of Italy (CNR)（网络（ICAR），意大利国家研究理事会（CNR））
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）
Convolutional neural network models for cancer type prediction based on gene expression
Greehey Children’s Cancer Research Institute（格里希儿童癌症研究所）；University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
King’s College London（伦敦国王学院）；Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（科技研究局信息与通信研究所）；Shenzhen University（深圳大学）；University of Manchester（曼彻斯特大学）
