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This book constitutes the refereed proceedings of the 16th International Workshop on Machine Learning in Medical Imaging, MLMI 2025, held in Conjunction with MICCAI 2025, Daejeon, South Korea, on September 23, 2025.
The 65 full papers included in this book were carefully reviewed and selected from 101 submissions. They focus on advanced scientific research covering topics such as deep learning, foundation models, generative learning, and statistical methods with their applications to computer-aided diagnosis, multi-modality fusion, image reconstruction, digital pathology, and large-scale data analytics.
List of contents
.- LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR.
.- Facial Model Assisted Shape Prediction for Orthognathic Surgery.
.- Joint Motion Correction of Multi-Atlas Functional Connectivity during Infancy.
.- 3D-ReVert: 3D Reconstruction of Vertebrae from a Single Radiograph for Minimally Invasive Spine Surgery.
.- VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation.
.- gResDM: A Graph-driven Residual Diffusion Model for Accelerating DWI Data Acquisition.
.- Semi-Supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model.
.- Regional Hausdorff Distance Losses for Medical Image Segmentation.
.- Identification of functional brain dynamics based on structural connectivity constrained functional time series.
.- MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representations.
.- Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach.
.- Leveraging self-supervised pretraining using transformers for enhanced lung nodule detection in CT scans.
.- From Action to Anatomy - Countering Data Scarcity with Video-Based Training for Ill-Posed MRI Problems.
.- Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challenges.
.- Synthesis of Abdominal Contrast-Enhanced CT using Diffusion-based Spatial Transform Control.
.- AMD-Mamba: A Phenotype-Aware Multi-Modal Framework for Robust AMD Prognosis.
.- DEnPL: Improved Classification in Imbalanced Medical Datasets via Data-Engineered Prototypical Metric Loss.
.- A Detail-preserving Latent Diffusion Model for Arbitrarily Accelerated MR Imaging.
.- TransGATNet: Hybrid Temporal-Frequency Features with Graph-Attention Transformers for Sleep Staging in OSA Patients.
.- U-DFA: A Unified DINOv2-Unet with Dual Fusion Attention for Multi-Dataset Medical Segmentation.
.- UniDis: Universal Distillation for Efficient and Personalized Pathology Diagnosis.
.- End-to-end Cortical Surface Reconstruction from Clinical Magnetic Resonance Images.
.- Brain Network Mamba: A Bi-directional State-Space Model for Brain Network Analysis on rs-fMRI.
.- Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning.
.- ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion.
.- TissueAgeNet: Quantitative Textual Guidance for Tissue Level Brain Age Estimation.
.- Surface-Guided Construction of 4D Volumetric Atlases of Fetal Brains.
.- Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection.
.- Feature Imputation for Missing Modalities in Multimodal Ultrasound.
.- CCMorph: Conditional Contrastive Learning for Unsupervised Medical Image Registration.
.- CAC-MAE: A Calcification-aware Masked Autoencoder for Cardiovascular Disease Risk Assessment on Low-Dose CT.
.- HiT-ULM: Hierarchical Temporal Dynamics Learning for Efficient Clinical Ultrasound Localization Microscopy.
.- Policy to Assist Iteratively Local Segmentation: Optimising Modality and Location Selection for Prostate Cancer Localisation.
.- Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses.
.- AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction based Semantic Patch Scoring.
.- Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations.
.- Medical Referring Image Segmentation: Addressing Multi-Lesion Reference and Annotation Uncertainty via Vision-Language Fusion.
.- Towards Generalizable Clinical Knowledge Discovery for Radiology Report Generation.
.- Weighted Mean Frequencies: a handcraft Fourier feature for 4D Flow MRI segmentation.
.- ConnecToMind: Connectome-Aware fMRI Decoding for Visual Image Reconstruction.
.- Segmentation of glioblastoma infiltration using hybrid labels from MRI and [18F]FET PET.
.- Pat
Summary
This book constitutes the refereed proceedings of the 16th International Workshop on Machine Learning in Medical Imaging, MLMI 2025, held in Conjunction with MICCAI 2025, Daejeon, South Korea, on September 23, 2025.
The 65 full papers included in this book were carefully reviewed and selected from 101 submissions. They focus on advanced scientific research covering topics such as deep learning, foundation models, generative learning, and statistical methods with their applications to computer-aided diagnosis, multi-modality fusion, image reconstruction, digital pathology, and large-scale data analytics.
