Medical Image Understanding and Analysis
Description
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.
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Content
.- 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.
