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The three-volume set LNCS 15916,15917 & 15918 constitutes the refereed proceedings of the 29th Annual Conference on Medical Image Understanding and Analysis, MIUA 2025, held in Leeds, UK, during July 15 17, 2025.
The 67 revised full papers presented in these proceedings were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections:
Part I: Frontiers in Computational Pathology; and Image Synthesis and Generative Artificial Intelligence.
Part II: Image-guided Diagnosis; and Image-guided Intervention.
Part III: Medical Image Segmentation; and Retinal and Vascular Image Analysis.
List of contents
.- Medical Image Segmentation.
.- TransE2UNet: Edge Guided TransEfficientUNET for Generalized Colon Polyp Segmentation from Endoscopy Images.
.- CA-Seg: An Attribute-based Medical Image Segmentation Framework for Unified Out-of-distributed Medical Image Segmentation.
.- TotalSegmentator 2D: A Tool for Rapid Anatomical Structure Analysis.
.- Promptable Cancer Segmentation Using Minimal Expert-curated Data.
.- SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours.
.- Semantic Segmentation with Spreading Scribbles.
.- A Hybrid Transformer-Graph Model for Multi-Class Lymph Node Segmentation in Histopathology.
.- Exploring Context-Switching in Medical Image Retrieval Using Segmentation Models.
.- Segmentation in Histopathology Utilising Simulated Masked Patches.
.- A Feature-Driven Acquisition Strategy Using Scale-Invariant Descriptors for Deep Active Learning in Preclinical CT Segmentation.
.- Quantifying Inter-Annotator Agreement and Generalist Model Limitations in Imaging Mass Cytometry Single Cell Segmentation.
.- Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer.
.- DRASU-Net: Dual-backbone and Residual Atrous Squeeze module-aided U-Net Model for Polyp Segmentation.
.- PolypDINO: Adapting DINOv2 for Domain Generalized Polyp Segmentation.
.- Intraoperative Segmentation Through Deep Learning and Mask Post-processing in Laparoscopic Liver Surgery.
.- Retinal and Vascular Image Analysis.
.- Hessian-based Deep Retinal Vessel Segmentation with Extremely Few Annotations.
.- Diffusion with Adversarial Fine-Tuning for Improving Rare Retinal Disease Diagnosis.
.- Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage.
.- Enhanced Coronary Artery Segmentation in CTCA Using Bridging Centreline Integration.
.- QD-RetNet: Efficient Retinal Disease Classification via Quantized Knowledge Distillation.
.- Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis.
.- GenVOG: A Diffusion Probabilistic Framework for Patient-Independent Pose-Guided Nystagmus Video-Oculography (VOG) Generation.
.- Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans.
Summary
The three-volume set LNCS 15916,15917 & 15918 constitutes the refereed proceedings of the 29th Annual Conference on Medical Image Understanding and Analysis, MIUA 2025, held in Leeds, UK, during July 15–17, 2025.
The 67 revised full papers presented in these proceedings were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections:
Part I: Frontiers in Computational Pathology; and Image Synthesis and Generative Artificial Intelligence.
Part II: Image-guided Diagnosis; and Image-guided Intervention.
Part III: Medical Image Segmentation; and Retinal and Vascular Image Analysis.
