Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI
Amsterdam University Medical Centers（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学研究所）；Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学研究所）
High-resolution medical images are beneficial for analysis but their acquisition may not always be feasible. Alternatively, high-resolution images can be created from low-resolution acquisitions using conventional upsampling methods, but such methods cannot exploit high-level contextual information contained in the images. Recently, better performing deep-learning based super-resolution methods have been introduced. However, these methods are limited by their supervised character, i.e. they require high-resolution examples for training. Instead, we propose an unsupervised deep learning semantic interpolation approach that synthesizes new intermediate slices from encoded low-resolution examples. To achieve semantically smooth interpolation in through-plane direction, the method exploits the latent space generated by autoencoders. To generate new intermediate slices, latent space encodings of two spatially adjacent slices are combined using their convex combination. Subsequently, the combined encoding is decoded to an intermediate slice. To constrain the model, a notion of semantic similarity is defined for a given dataset. For this, a new loss is introduced that exploits the spatial relationship between slices of the same volume. During training, an existing in-between slice is generated using a convex combination of its neighboring slice encodings. The method was trained and evaluated using publicly available cardiac cine, neonatal brain and adult brain MRI scans. In all evaluations, the new method produces significantly better results in terms of Structural Similarity Index Measure and Peak Signal-to-Noise Ratio (p< 0.001 using one-sided Wilcoxon signed-rank test) than a cubic B-spline interpolation approach. Given the unsupervised nature of the method, high-resolution training data is not required and hence, the method can be readily applied in clinical settings.
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
Columbia University（哥伦比亚大学）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）；University of Maryland, College Park（马里兰大学帕克分校）；University of California, Irvine（加州大学欧文分校）；University of Rochester（罗切斯特大学）；Universidade Federal de São Paulo（圣保罗联邦大学）；Duke University（杜克大学）；Emory University（埃默里大学）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院（斯科尔科沃科技学院））
Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yetonly a few applications are now in clinical use and one of the reasons for that is poor transferability of themodels to data from different sources or acquisition domains. Development of new methods and algorithms forthe transfer of training and adaptation of the domain in multi-modal medical imaging data is crucial for thedevelopment of accurate models and their use in clinics. In present work, we overview methods used to tackle thedomain shift problem in machine learning and computer vis…
Continual atlas-based segmentation of prostate MRI
Technical University of Darmstadt（达姆施塔特工业大学）；Stanford University（斯坦福大学）；University of Cologne（科隆大学）；University of Frankfurt（法兰克福大学）；University Medical Center Mainz（美因茨大学医学中心）
Arizona State University（亚利桑那州立大学）；Mayo Clinic（梅奥诊所）
Medical images are naturally associated with rich semantics about the human anatomy, reflected in an abundance of recurring anatomical patterns, offering unique potential to foster deep semantic representation learning and yield semantically more powerful models for different medical applications. But how exactly such strong yet free semantics embedded in medical images can be harnessed for self-supervised learning remains largely unexplored. To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of th…
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna（人工智能研究所，医学数据科学中心，维也纳医科大学）；Department of Ophthalmology and Optometry, Medical University of Vienna（眼科学与视光学系，维也纳医科大学）；Ophthalmic Image Analysis Group (OPTIMA), Medical University of Vienna（眼科影像分析组（OPTIMA），维也纳医科大学）；BioMedIA, Department of Computing, Imperial College London（BioMedIA，计算系，伦敦帝国理工学院）；Chair for AI in Healthcare and Medicine, Technical University of Munich（医学与健康人工智能教授职位，慕尼黑技术大学）；Moorfields National Institute for Health and Care Biomedical Research Centre, Moorfields Eye Hospital（莫尔菲尔兹国家健康与护理生物医学研究中心，莫尔菲尔兹眼科医院）
PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation
Akian College of Science and Engineering, American University of Armenia（阿塞拜疆美国大学科学与工程学院）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）
