Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2

Contrastive Representations for Continual Learning of Fine-grained Histology Images.- Learning Transferable 3D-CNN for MRI-based Brain Disorder Classification from Scratch: An Empirical Study.- Knee Cartilages Segmentation Based on Multi-scale Cascaded Neural Networks.- Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation.- Interpretable Histopathology Image Diagnosis via Whole Tissue Slide Level Supervision.- Variational Encoding and Decoding for Hybrid Supervision of Registration Network.- Multiresolution Registration Network (MRN) Hierarchy with Prior Knowledge Learning.- Learning to Synthesize 7T MRI from 3T MRI with Few Data by Deformable Augmentation.- Rethinking Pulmonary Nodule Detection in Multi-view 3D CT Point Cloud Representation.- End-to-end lung nodule detection framework with model-based feature projection block.- Learning Structure from Visual SemanticFeatures and Radiology Ontology for LymphNode Classification on MRI.- Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment.- Cell Counting by a Location-Aware Network.- Exploring Gyro-Sulcal Functional Connectivity Differences across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks.- StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis.- Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images.- Transfer learning with a layer dependent regularization for medical image segmentation.- Multi-Scale Self-Supervised Learning for Multi-Site Pediatric Brain MR Image Segmentation with Motion/Gibbs Artifacts.- Deep active learning for dual-view mammogram analysis.- Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound.- Semi-supervised Learning Regularized by Adversarial Perturbation and Diversity Maximization.- TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes.- A Recurrent Two-stage Anatomy-guided Network for Registration of Liver DCE-MRI.- Learning Infancy Brain Developmental Connectivity for the Cognitive Score Prediction.- Hierarchical 3D Feature Learning for Pancreas Segmentation.- Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction.- Diagnosis of Hippocampal Sclerosis from Clinical Routine Head MR Images using Structure-Constrained Super-Resolution Network.- U-Net Transformer: Self and Cross Attention for Medical Image Segmentation.- Pre-biopsy multi-class classification of breast lesion pathology in mammograms.- Co-Segmentation of Multi-Modality Spinal Images Using Channel and Spatial Attention.- Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data.- STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains.- Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment.- MIST GAN: Modality Imputation using Style Transfer for MRI.- Biased Extrapolation in Latent Space for Imbalanced Deep Learning.- 3DMeT: 3D Medical Image Transformer for Knee Cartilage Defect Assessment.- A Gaussian Process Model for Unsupervised Analysis of High Dimensional Shape Data.- Standardized Analysis of Kidney Ultrasound Images for the Prediction of Pediatric Hydronephrosis Severity.- Automated deep learning-based detection of osteoporotic fractures in CT images.- GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation.- Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis.- Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling.- TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising.- Self-supervised Mean Teacher for Semi-supervisedChest X-ray Classification.- VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning.- Using Spatio-Tem