Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（汉堡工业大学医疗技术与智能系统研究所）；jung diagnostics GmbH（jung diagnostics GmbH公司）；Hamburg University of Technology（汉堡工业大学）
Marcel Bengs, Finn Behrendt, Max-Heinrich Laves, Julia Krüger, Roland Opfer, Alexander Schlaefer
Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results to provide a quick, initial assessment. So far, these methods only rely on the visual appearance of healthy brain anatomy for anomaly detection. Another biomarker for abnormal brain development is the deviation between the brain age and the chronological age, which is unexplored in combination with UAD. We propose deep learning for UAD in 3D brain MRI considering additional age information. We analyze the value of age information during training, as an additional anomaly score, and systematically study several architecture concepts. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction. We use clinical T1-weighted MRIs of 1735 healthy subjects and the publicly available BraTs 2019 data set for our study. Our novel approach significantly improves UAD performance with an AUC of 92.60% compared to an AUC-score of 84.37% using previous approaches without age information.
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs
Stanford University（斯坦福大学）；University of Missouri（密苏里大学）；Missouri Institute of Mental Health（密苏里精神卫生研究所）；Weill Cornell Medicine（威尔康奈尔医学院）；SRI International（SRI国际研究所）
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（汉堡工业大学医学技术与智能系统研究所）；jung diagnostics GmbH（jung diagnostics 有限公司）
The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset
University of California San Francisco（加州大学旧金山分校）；University of California, San Diego（加州大学圣地亚哥分校）；University of Pennsylvania（宾夕法尼亚大学）；Duke University（杜克大学）
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院Martinos生物医学影像中心）；Massachusetts Institute of Technology（麻省理工学院）；Cornell University（康奈尔大学）；University College London（伦敦大学学院）
SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions
Dept. of Math and Computer Science（数学与计算机科学系）；Department of Neuroinflammation（神经炎症系）；School of Physics, Engineering and CS（物理、工程与计算机科学学校）；University of Hertfordshire（赫特福德大学）
Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI
Florida Institute of Technology（佛罗里达理工学院）；Daffodil International University（达福德国际大学）；University of North Texas（北德克萨斯大学）；The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Evaluation of 3D Counterfactual Brain MRI Generation
Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA Department of Radiology, Stanford University, Stanford, CA, USA Dept. of Psychiatry \& Behavioral Sciences, Stanford University, Stanford, CA, USA Department of Computer Science, Stanford University, Stanford, CA, USA Department of Bioengineering, Stanford University, Stanford, CA, USA Department of Electrical Engineering, Stanford University, Stanford, CA, USA
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network
Vingroup Big Data Institute (VinBigdata)（越南 VinBigdata 大数据研究院）；Hanoi University of Science and Technology (HUST)（河内科技大学）；University of Engineering and Technology (UET), VNU（越南国立大学工程技术大学）
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images
University College Dublin（都柏林大学学院）；Université Paris-Est Créteil Val de Marne - Université Paris 12（巴黎东-马恩拉瓦莱大学（巴黎第十二大学））；ADAPT SFI Research Centre（ADAPT SFI研究中心）
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Lausanne University Hospital（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；King’s College London（伦敦国王学院）；FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
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）
Automating Whole Brain Histology to MRI Registration: Implementation of a Computational Pipeline
Escola Politécnica, Universidade de São Paulo（圣保罗大学理工学院）；Brain Institute, IIEP Albert Einstein（阿尔伯特·爱因斯坦教育与研究学院脑研究所）；Faculdade de Medicina, Universidade de São Paulo（圣保罗大学医学院）；University of California San Francisco（加州大学旧金山分校）；University of Würzburg（维尔茨堡大学）；Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST（麻省总医院/哈佛-麻省理工健康科学与技术部阿西诺拉·A·马丁诺斯生物医学成像中心）
WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIs
Department of Radiology, Weill Cornell Medicine, New York, NY, USA（放射科，韦尔·科恩医学中心，纽约，纽约州，美国）；Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学，斯坦福，加利福尼亚州，美国）；Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA（电气与计算机工程系，康奈尔大学，伊萨卡，纽约州，美国）
AutoRG-Brain: Grounded Report Generation for Brain MRI
University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University（上海交通大学附属第六人民医院）
From Pre- to Intra-operative MRI: Predicting Brain Shift in Temporal Lobe Resection for Epilepsy Surgery
Surgical and Interventional Engineering（外科与介入工程）；King’s College London（伦敦国王学院）；National Hospital for Neurology and Neurosurgery（神经外科医院）；NHS（国家健康服务）；Neurosurgery, King’s College Hospital（神经外科，伦敦国王学院医院）；Fondazione IRCCS Ca’ Granda（IRCCS Ca’ Granda基金会）；Ospedale Maggiore di Milano（米兰大医院）
