Medical Image Learning with Limited and Noisy Data

Efficient and Robust Annotation Strategies.- Heatmap Regression for Lesion Detection using Pointwise Annotations.-.- Partial Annotations for the Segmentation of Large Structures with Low Annotation.-.- Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade Assessment.- Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels.- Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction.- Weakly-supervised, Self-supervised, and Contrastive Learning.- Universal Lesion Detection and Classification using Limited Data and Weakly-Supervised Self-Training.- BoxShrink: From Bounding Boxes to Segmentation Masks.- Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis.- SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRI.- Optimizing Transformations for Contrastive Learning in a Differentiable Framework.-Stain-based Contrastive Co-training for Histopathological Image Analysis.- Active and Continual Learning.- CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification.- Real-time Data Augmentation using Fractional Linear Transformations in Continual Learning.- DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures.- Transfer Representation Learning.- Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning.- Asymmetry and Architectural Distortion Detection with Limited Mammography Data.- Imbalanced Data and Out-of-distribution Generalization.- Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT.- CVAD: An Anomaly Detector for Medical Images Based on Cascade.- Approaches for Noisy, Missing, and Low Quality Data.- Visual Field Prediction with Missing and Noisy Data Based on Distance-based Loss.- Image Quality Classification for Automated Visual Evaluation of