This book constitutes the proceedings of the 15th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2024, Marrakesh, Morocco, on October 6, 2024.The 63 full papers presented in this volume were carefully reviewed and selected from 100 submissions.

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.