Deep Generative Models, and Data Augmentation, Labelling, and Imperfections: First Workshop, DGM4MICCAI 2021 and First Workshop, DALI 2021, held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021,proceedings - Engelhardt, Sandy; Oksuz, Ilkay; Zhu, Dajiang; Yuan, Yixuan; Mukhopadhyay, Anirban; Heller, Nicholas; Huang, Sharon Xiaolei; Nguyen, Hien; Sznitman, Raphael; Xue, Yuan (Image Processing, Computer Vision, Pattern Recognition, and Graphics, 2021)
DGM4MICCAI 2021 - Image-to-Image Translation, Synthesis -- Frequency-Supervised MRI-to-CT Image Synthesis -- Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain -- 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images -- Bridging the gap between paired and unpaired medical image translation -- Conditional generation of medical images via disentangled adversarial inference. -CT-SGAN: Computed Tomography Synthesis GAN -- Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference -- CaCL: class-aware codebook learning for weakly supervised segmentation on diffuse image patterns -- BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification -- Evaluating GANs in medical imaging -- DGM4MICCAI 2021 - AdaptOR challenge -- Improved Heatmap-based Landmark Detection -- Cross-domain Landmarks Detection in Mitral Regurgitation -- DALI 2021 -- Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph -- Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency -- One-shot Learning for Landmarks Detection -- Compound Figure Separation of Biomedical Images with Side Loss -- Data Augmentation with Variational Autoencoders and Manifold Sampling -- Medical image segmentation with imperfect 3D bounding boxes -- Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images -- How Few Annotations are Needed for Segmentation using a Multi-planar U-Net? -- FS-Net: A New Paradigm of Data Expansion for Medical Image Segmentation -- An Efficient Data Strategy for the Detection of Brain Aneurysms from MRA with Deep Learning -- Evaluation of Active Learning Techniques on Medical Image Classification with Unbalanced Data Distributions -- Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation -- Label Noise in Segmentation Networks : Mitigation Must Deal with Bias -- DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization -- MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation.