Medical Image Learning with Limited and Noisy Data

Efficient Annotation and Training Strategies.- Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-quality Annotations.- ScribSD: Scribble-supervised Fetal MRI Segmentation based on Simultaneous Feature and Prediction Self-Distillation.- Label-efficient Contrastive Learning-based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images.- Affordable Graph Neural Network Framework using Topological Graph Contraction.- Approaches for Noisy, Missing, and Low Quality Data.- Dual-domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-angle Reconstruction of Low-dose Cardiac SPECT.- A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging.- COVID-19 Lesion Segmentation Framework for the Contrast-enhanced CT in the Absence of Contrast-enhanced CT Annotation.- Feasibility of Universal Anomaly Detection without Knowingthe Abnormality in Medical Image.- Unsupervised, Self-supervised, and Contrastive Learning.- Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection.- FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation.- Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy.- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.- SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction.- Robust Unsupervised Image to Template Registration Without Image Similarity Los.- A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue.- Weakly-supervised, Semi-supervised, and Multitask Learning.- Combining Weakly Supervised Segmentation with Multitask Learning