Multi-task Learning Approach for Intracranial Hemorrhage Prognosis
Advanced Computing and e-Science Group, Institute of Physics of Cantabria (IFCA), CSIC - UC（先进计算与电子科学组，坎塔布里亚物理研究所（IFCA），CSIC-UC）；AI Technology for Life, Department of Computing and Information Sciences, Utrecht University（人工智能技术与生命，计算与信息科学系，乌得勒支大学）；Department of Radiology, The Netherlands Cancer Institute（放射科，荷兰癌症研究所）；Servicio de Radiología, Hospital Reina Sofía（放射科，雷娜·索菲亚医院）；Servicio de Radiología, Hospital Universitario Rey Juan Carlos（放射科，雷昂·卡洛斯大学医院）；Departamento de Tecnologías de La Información, Universidad CEU San Pablo（信息技术部，CEU桑帕布洛大学）
Miriam Cobo, Amaia Pérez del Barrio, Pablo Menéndez Fernández-Miranda, Pablo Sanz Bellón, Lara Lloret Iglesias, Wilson Silva
备注：
16 pages. Accepted at Machine Learning in Medical Imaging Workshop @ MICCAI 2024 (MLMI2024). This is the submitted manuscript with added link to github repo, funding acknowledgements and authors' names and affiliations. No further post submission improvements or corrections were integrated. Final version not published yet
发表信息：
Machine Learning in Medical Imaging: 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings, Part II
Prognosis after intracranial hemorrhage (ICH) is influenced by a complex interplay between imaging and tabular data. Rapid and reliable prognosis are crucial for effective patient stratification and informed treatment decision-making. In this study, we aim to enhance image-based prognosis by learning a robust feature representation shared between prognosis and the clinical and demographic variables most highly correlated with it. Our approach mimics clinical decision-making by reinforcing the model to learn valuable prognostic data embedded in the image. We propose a 3D multi-task image model to predict prognosis, Glasgow Coma Scale and age, improving accuracy and interpretability. Our method outperforms current state-of-the-art baseline image models, and demonstrates superior performance in ICH prognosis compared to four board-certified neuroradiologists using only CT scans as input. We further validate our model with interpretability saliency maps. Code is available at https://github.com/MiriamCobo/MultitaskLearning_ICH_Prognosis.git.
GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration
Hangzhou Dianzi University（杭州电子科技大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Ping An Technology（平安科技）；Longgang District Central Hospital of Shenzhen（深圳市龙岗区中心医院）；National Health Data Institute, Shenzhen（深圳国家健康数据研究院）
Vision Transformer for Intracranial Hemorrhage Classification in CT Scans Using an Entropy-Aware Fuzzy Integral Strategy for Adaptive Scan-Level Decision Fusion
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（塔里斯坦大学电气与计算机工程学院）；Department of Computer Science and Engineering, Sejong University（世宗大学计算机科学与工程系）
Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan
Capital Medical University（首都医科大学）；China National Clinical Research Center for Neurological Diseases（国家神经系统疾病临床医学研究中心）；Tencent（腾讯）；Shandong Jining Medical College（山东济宁医学院）
背景资料：开发一种人工智能系统，该系统可以基于非造影CT（NCCT）扫描准确识别急性非创伤性颅内出血（ICH）病因，并研究临床医生是否可以在诊断环境中从中受益。材料与方法：深度学习模型是使用2011年1月至2018年4月期间收集的1868例符合条件的非创伤性ICH NCCT扫描结果开发的。我们在2018年4月之后收集的两个独立数据集（TT 200和SD 98）上测试了该模型。该模型的诊断性能与临床医生的性能进行了比较。我们进一步设计了一项模拟研究，以比较临床医生在有和没有深度学习系统增强的情况下的表现。结果如下：所提出的深度学习系统实现了0.986的受试者工作曲线下面积（95% CI 0.967-1.000）动脉瘤，0.952（0.917-0.987）高血压出血，0.950（0.860-1.000）动静脉畸形（AVM），0.749 TT 200数据集中Moyamoya病（MMD）为0.586-0.912，海绵状血管畸形（CM）为0.837（0.704-0.969），其他原因为0.839（0.722-0.959）。在90%的特异性水平下，我们的模型对动脉瘤和AVM诊断的敏感性分别为97.1%和90.9%。该模型在独立数据集SD 98中也显示出令人印象深刻的普遍性。临床医生通过拟议的系统增强在某些出血病因诊断的灵敏度、特异性和准确性方面实现了显著改善。结论：提出的深度学习算法可以成为基…
SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学北京天坛医院）
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection
Northwestern University（西北大学）；University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））
A Prospective Randomized Clinical Trial for Measuring Radiology Study Reporting Time on Artificial Intelligence-Based Detection of Intracranial Hemorrhage in Emergent Care Head CT
Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy
Research Institute for Signals, Systems and Computational Intelligence, sinc(i), CONICET, FICH-UNL（信号、系统与计算智能研究所 (sinc(i))，国家科学与技术研究理事会 (CONICET)，卢汉国立大学工程学院 (FICH-UNL)）；University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）
Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation
ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University Hospital of Tübingen（蒂宾根大学医院）；Kantonsspital Winterthur（温特图尔州立医院）；GlaxoSmithKline（葛兰素史克）
