Machine-based brain tumor segmentation can help doctors make better diagnoses. However, the complex structure of brain tumors and expensive pixel-level annotations present challenges for automatic tumor segmentation. In this paper, we propose a counterfactual generation framework that not only achieves exceptional brain tumor segmentation performance without the need for pixel-level annotations, but also provides explainability. Our framework effectively separates class-related features from class-unrelated features of the samples, and generate new samples that preserve identity features while altering class attributes by embedding different class-related features. We perform topological data analysis on the extracted class-related features and obtain a globally explainable manifold, and for each abnormal sample to be segmented, a meaningful normal sample could be effectively generated with the guidance of the rule-based paths designed within the manifold for comparison for identifying the tumor regions. We evaluate our proposed method on two datasets, which demonstrates superior performance of brain tumor segmentation. The code is available at https://github.com/xrt11/tumor-segmentation.
High Accuracy Tumor Diagnoses and Benchmarking of Hematoxylin and Eosin Stained Prostate Core Biopsy Images Generated by Explainable Deep Neural Networks
Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）；Boston University School of Medicine（波士顿大学医学院）；VA Boston Healthcare（波士顿退伍军人医疗中心）
Demystifying Deep Learning-based Brain Tumor Segmentation with 3D UNets and Explainable AI (XAI): A Comparative Analysis
School of Artificial Intelligence and Robotics, Xiamen University Malaysia（人工智能与机器人学院，马来西亚厦门大学）；School of Computing and Data Science, Xiamen University Malaysia（计算与数据科学学院，马来西亚厦门大学）；Georgia Institute of Technology（佐治亚理工学院）；School of Social and Health Sciences, James Cook University（社会科学与健康科学学院，詹姆斯库克大学）；College of Healthcare Sciences, James Cook University（健康科学学院，詹姆斯库克大学）
Fusion-Based Brain Tumor Classification Using Deep Learning and Explainable AI, and Rule-Based Reasoning
University of Rochester（罗切斯特大学）；Islamic Azad University（伊斯兰阿扎德大学）；Azad University（阿扎德大学）；Deggendorf Institute of Technology（代根多夫理工学院）；The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study
Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Medical Center Rotterdam（埃拉斯姆斯MC癌症研究所放射学与核医学部，埃因霍温医学院鲁特沃特分校）；Department of Surgical Oncology, Erasmus MC Cancer Institute, University Medical Center Rotterdam（埃拉斯姆斯MC癌症研究所外科肿瘤部，埃因霍温医学院鲁特沃特分校）；Department of Pathology, Erasmus MC Cancer Institute, University Medical Center Rotterdam（埃拉斯姆斯MC癌症研究所病理学部，埃因霍温医学院鲁特沃特分校）；Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam（荷兰癌症研究所医学肿瘤部，阿姆斯特丹）；Department of Medical Oncology, Leiden University Medical Center, Leiden（莱顿大学医学中心医学肿瘤部，莱顿）；Department of Medical Oncology, Radboud University Medical Centre, Nijmegen（拉德堡德大学医学中心医学肿瘤部，尼日梅根）；Department of Medical Oncology, University Medical Center Groningen, University of Groningen（格罗宁根大学医学中心医学肿瘤部，格罗宁根）；Department of Medical Oncology, Netherlands Cancer Institute, Antoni Van Leeuwenhoek（荷兰癌症研究所医学肿瘤部，安托万·弗莱明）
Bridging visual saliency and large language models for explainable deep learning in medical imaging
Department of Mathematics and Computer science（数学与计算机科学系）；Institute of Cognitive Science（认知科学研究所）；Rhodes University（罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（理论与计算科学国家研究所）
From Explanations to Architecture: Explainability-Driven CNN Refinement for Brain Tumor Classification in MRI
American International Brac University Faculty of Psychology University-Bangladesh（美国国际布拉大学心理学学院（孟加拉国））；Shinawatra University Department of Computer Science（辛纳瓦特大学计算机科学系）；American International North South University（美国国际北南大学）；American International University–Bangladesh Department of Computer Science（美国国际大学–孟加拉国计算机科学系）；Multimedia University Department of Computer Science（多媒体大学计算机科学系）
Evaluating Single and Multi-Omics Based Explainable Artificial Intelligence (MOXAI) for Molecular Subclass Classification of Adult-Type Diffuse Gliomas
Zero-shot segmentation of skin tumors in whole-slide images with vision-language foundation models
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-Tech), Universitat Politécnica de Valencia（人类中心技术大学研究机构（HUMAN-Tech）、西班牙巴塞罗那理工大学）；Artikode Intelligence S.L.（Artikode Intelligence公司）
Automatic and explainable grading of meningiomas from histopathology images
Technische Hochschule Ingolstadt（英戈尔施塔特工业大学）；University Hospital Erlangen（埃尔朗根大学医院）；University of Veterinary Medicine Vienna（维也纳兽医大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）
Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview
School of Artificial Intelligence, Guilin University of Electronic Technology（桂林电子科技大学人工智能学院）；École de Technologie Supérieure (ETS)（高等技术学院）；College of Computer Science and Engineering, Taibah University（塔伊布大学计算机科学与工程学院）；College of Computer Science and Engineering, Jeddah University（吉达大学计算机科学与工程学院）
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data
Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Baystate Medical Center（贝斯州医疗中心）；UMass Chan Medical School - Baystate Campus（马萨诸塞大学陈医学院贝斯州校区）
本文面向撒哈拉以南非洲成人患者，提出融合 UNet3D、ONet3D、SphereNet3D 及改进损失函数的十一模型集成脑肿瘤分割方法，在 BraTS AFRICA 数据上取得优于单模型的分割性能。
BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
School of Mathematics, Physics and Computing, University of Southern Queensland（南方昆士兰大学数学、物理与计算学院）；School of Engineering, University of Southern Queensland（南方昆士兰大学工程学院）
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images
School of Science, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）理学院）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）
Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods
Department of Communication Engineering University of Málaga（通信工程系马德里大学）；Department of Clinical Neurosciences University of Cambridge（临床神经科学系剑桥大学）；Department of Neuroscience University of Padova（神经科学系帕多瓦大学）；Department of Signal Theory, Telematics and Communications, University of Granada（信号理论、电信与通信系格拉纳达大学）；Department of Computer Science and Technology University of Cambridge（计算机科学与技术系剑桥大学）；Department of Psychiatry University of Cambridge（精神病学系剑桥大学）；NHS Foundation Trust United Kingdom（国家健康服务基金会信托英国）
Multimodal Slice Interaction Network Enhanced by Transfer Learning for Precise Segmentation of Internal Gross Tumor Volume in Lung Cancer PET/CT Imaging
Dept. of Biomedical Engineering Johns Hopkins University（生物医学工程系约翰霍普金斯大学）；Molecular Radiation Sciences Johns Hopkins University（分子放射科学约翰霍普金斯大学）；Dept. of Surgery University of Minnesota（外科系明尼苏达大学）；Dept. of Biomedical Engineering University of Virginia（生物医学工程系弗吉尼亚大学）；Comprehensive Cancer Center Mayo Clinic（综合癌症中心梅奥诊所）
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China（电子科技大学光电科学与工程学院）；Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；The Hospital for Sick Children (SickKids)（SickKids 儿童医院（病童医院））
Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis
L’Institut National de la Santé et de la Recherche Médicale (INSERM)（法国国家健康与医学研究院）；Centre National de la Recherche Scientifique (CNRS)（法国国家科学研究中心）；University of Toulouse III - Paul Sabatier（图卢兹第三大学 - 保罗·萨巴蒂埃大学）；Institut Universitaire du Cancer-Oncopole de Toulouse (IUCT)（图卢兹大学癌症研究所 - 昂科波勒）；Centre Hospitalier Universitaire de Bordeaux（波尔多大学医院中心）；Université de Bordeaux（波尔多大学）；University of Toulouse - Institut de Recherche en Informatique de Toulouse (IRIT)（图卢兹大学 - 图卢兹计算机科学研究所）；University of Oxford（牛津大学）；Kennedy Institute of Rheumatology (KIR)（肯尼迪风湿病研究所）；Leibniz Institute for New Materials（莱布尼茨新材料研究所）
Learning to Select Like Humans: Explainable Active Learning for Medical Imaging
AI Research, Department of Computer Science, University of South Dakota（人工智能研究，计算机科学系，南达科他大学）；Department of Computer Science and Software Engineering, Auburn University（计算机科学与软件工程系，阿伯丁大学）
Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation
School of Mathematical Science（数学科学学院）；Academy for Multidisciplinary Studies（交叉学科研究院）；International Information and Software Institute（国际信息与软件学院）；Dalian University of Technology（大连理工大学）
