Machine Learning in Medical Imaging

A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation.- Generalizable Lymph Node Metastasis Prediction in Pancreatic Cancer.- IRUM: An Image Representation and Unified Learning Method for Breast Cancer Diagnosis from Multi-view Ultrasound Images.- Classification, Regression and Segmentation directly from k-Space in Cardiac MRI.- DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography.- Mitral Regurgitation Recogniton based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification.- Deep Reinforcement Learning with Multiple Centerline-Guidance for Localization of Left Atrial Appendage Orifice from CT Images.- Lung-CADex: Fully automatic Zero-Shot Detection & Classification of Lung Nodules in Thoracic CT Images.- CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression.- Vision Transformer Model for Automated End-to-End Radiographic Assessment of Joint Damage in Psoriatic Arthritis.- CorticalEvolve: Age-Conditioned Ordinary Differential Equation Model for Cortical Surface Reconstruction.- CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning.- Atherosclerotic plaque stability prediction from longitudinal ultrasound images.- Leveraging IHC Staining to Prompt HER2 Status Prediction from HE-Stained Histopathology Whole Slide Images.- VIMs: Virtual Immunohistochemistry Multiplex staining via Text-to-Stain Diffusion Trained on Uniplex Stains.- Structural-Connectivity-guided Functional Connectivity Representation for Multi-modal Brain Disease Classification.-Clinical Brain MRI Super-Resolution with 2D Slice-Wise Diffusion Model.- Low-to-high Frequency Progressive K-Space Learning for MRI Reconstruction.- LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging.- 7T-like T1-weighted and TOF MRI synthesis from 3T MRI with Multi-contrast Complementary Deep Learning.- A Probabilistic Hadamard U-Net for MRI Bias Field Correction.- Structure-Preserving Diffusion Model for Unpaired Medical Image Translation.- Simultaneous Image Quality Improvement and Artefacts Correction in Accelerated MRI.- Full-TrSUN: A Full-Resolution Transformer UNet for high quality PET image synthesis.- TS-SR3: Time-strided Denoising Diffusion Probabilistic Model for MR Super-resolution.- PDM: A Plug-and-Play Perturbed Multi-path Diffusion Module for Simultaneous Medical Image Segmentation Improvement and Uncertainty Estimation.- DyNo: Dynamic Normalization based Test-Time Adaptation for 2D Medical Image Segmentation.-Accurate Delineation of Cerebrovascular Structures from TOF-MRA with Connectivity-Reinforced Deep Learning.- Learning Instance-Discriminative Pixel Embeddings Using Pixel Triplets.- Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound.- Domain Influence in MRI Medical Image Segmentation: spatial versus k-space inputs.- Enhanced Small Liver Lesion Detection and Segmentation Using a Size-focused Multi-model Approach in CT Scans.- Generation and Segmentation of Simulated Total-Body PET Images.- Integrating Convolutional Neural Network and Transformer for Lumen Prediction along the Aorta Sections.- CSSD: Cross-Supervision and Self-Denoising for Hybrid-Supervised Hepatic Vessel Segmentation.- Calibrated Diverse Ensemble Entropy Minimization for Robust Test-Time Adaptation in Prostate Cancer Detection.- SpineStyle: Conceptualizing Style Transfer for Image-Guided Spine Surgery on Radiographs.- SGSR: Structure-Guided Multi-Contrast MRI Super-Resolution via Spatio-Frequency Co-Query Attention.- Knowledge Distillation based Dual-Branch Network for Whole Slide Image Analysis.- DHSampling: Diversity-based Hyperedge Sampling in GNN Learning with Application to Medical Imaging Classification.