Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images. However, data quantity and quality remain a key determinant of, and a significant limit on, the potential of such applications. In our previous work, we explored synthesis of anatomic and molecular MR image network (SAMR) in patients with post-treatment malignant glioms. Now, we extend it and propose Confidence Guided SAMR (CG-SAMR) that synthesizes data from lesion information to multi-modal anatomic sequences, including T1-weighted (T1w), gadolinium enhanced T1w (Gd-T1w), T2-weighted (T2w), and fluid-attenuated inversion recovery (FLAIR), and the molecular amide proton transfer-weighted (APTw) sequence. We introduce a module which guides the synthesis based on confidence measure about the intermediate results. Furthermore, we extend the proposed architecture for unsupervised synthesis so that unpaired data can be used for training the network. Extensive experiments on real clinical data demonstrate that the proposed model can perform better than the state-of-theart synthesis methods.
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Oslo University Hospital（奥斯陆大学医院）；Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto Universitario de Tecnologías de la Información y Comunicaciones（信息技术与通信大学学院）
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection
Carnegie Vanguard High School（卡内基先锋高中）；Devon Preparatory High School（德文预备高中）；Lake Travis High School（特拉维斯湖高中）；Irvington High School（欧文顿高中）；Algoverse AI Research（Algoverse AI研究机构）
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine（印第安纳大学医学院计算病理学系）；Department of Electrical and Systems Engineering, Washington University in St. Louis（圣路易斯华盛顿大学电气与系统工程系）；GE HealthCare（GE医疗）；Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Penn（宾夕法尼亚大学综合诊断人工智能与数据科学中心及生物医学图像计算与分析中心）
King's College London（伦敦国王学院）；Imperial College Healthcare NHS Trust（帝国理工医疗保健 NHS 信托）；St George's University Hospitals NHS Foundation Trust（圣乔治大学医院 NHS 基金会信托）；Guy’s & St. Thomas’ NHS Foundation Trust（盖伊和圣托马斯 NHS 信托）；King's College Hospital NHS Foundation Trust（国王学院医院 NHS 基金会信托）
Towards Machine Learning-based Quantitative Hyperspectral Image Guidance for Brain Tumor Resection
University of British Columbia（不列颠哥伦比亚大学）；University Hospital of Münster（明斯特大学医院）；Macquarie University（麦考瑞大学）；Hokkaido Medical Center, National Hospital Organization（国立医院机构北海道医疗中心）
Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection
Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；The University of Texas MD Anderson Graduate School of Biomedical Science（德克萨斯大学MD安德森生物医学研究生院）
MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education
Faculty of Medicine, American University of Beirut（贝鲁特美国大学医学院）；American University of Beirut Medical Center（贝鲁特美国大学医学中心）；Department of Diagnostic Radiology, American University of Beirut Medical Center（贝鲁特美国大学医学中心诊断放射科）
在他们的成长期，放射学实习生被要求每月解读数百张乳房X光片，目的是能够辨别出区分良性和恶性病变的细微模式。不幸的是，医学法律和技术障碍使得难以访问和查询用于训练的医学图像。在本文中，我们训练一个生成对抗网络（GAN）来合成512 x 512的高分辨率乳房X线照片。所得到的模型导致高级别特征（例如，标准乳房X线摄影视图和乳房病变的性质）的无监督分离，在所生成的图像中具有随机变化（例如，乳房脂肪组织、钙化），从而实现合成图像的用户控制的全局和局部属性编辑。我们证明了该模型的能力，以生成解剖学和医学相关的乳房X线照片，实现了0.54的平均AUC在双盲研究中的四个专家乳房X线摄影放射科医生，以区分生成的和真实的图像，归因于高视觉质量的合成和编辑的乳房X线照片，并在推进和促进医学教育的潜在用途。
A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma
University Hospital Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔兰根-欧洲肿瘤研究所综合癌症中心）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；Institute of Neuroradiology, University Hospital Erlangen（埃尔兰根大学医院神经放射学研究所）；Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学埃尔兰根大学医院神经外科）；Universität Würzburg（维尔茨堡大学）
The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection
Computer Vision and Robotics Research Institute, University of Girona（赫罗纳大学计算机视觉与机器人研究所）；Hospital Clínic Barcelona, Universitat de Barcelona（巴塞罗那大学巴塞罗那临床医院）；Frazer Institute, The University of Queensland（昆士兰大学弗雷泽研究所）；University of Trieste（的里雅斯特大学）；ISAHIT；Canfield Scientific, Inc.（坎菲尔德科学公司）；HUN-REN Institute for Computer Science and Control（HUN-REN计算机科学与控制研究所）
