Medical Image Understanding and Analysis : 29th Annual Conference, MIUA 2025, Leeds, UK, July 15-17, 2025, Proceedings, Part I (Lecture Notes in Computer Science 15916) (2025. x, 310 S. X, 310 p. 235 mm)

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Medical Image Understanding and Analysis : 29th Annual Conference, MIUA 2025, Leeds, UK, July 15-17, 2025, Proceedings, Part I (Lecture Notes in Computer Science 15916) (2025. x, 310 S. X, 310 p. 235 mm)

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Full 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.

Contents

.- Frontiers in Computational Pathology.

.- Transductive Survival Ranking for Pan-cancer Automatic Risk Stratification using Whole Slide Images.

.- Benchmarking Histopathology Foundation Models in a Multi-center Dataset for Skin Cancer Subtyping.

.- MitoNet: Efficient Ki-67 Detection in H\&E-Stained Images.

.- ASTER: Automated Segmentation of Endometrial Histology Images for Reproductive Health Assessment.

.- Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H\&E Images.

.- SMatt-DINO: Spatially Aware Masked Attention Network for High Resolution Brain Image Classification.

.- Persistent Homology and Gabor Features Reveal Inconsistencies Between Widely Used Colorectal Cancer Training and Testing Datasets.

.- SWIFT-Reg: Slide-Wide Intelligent Feature-based Tissue Registration.

.- Learnable Moran's Index for Modeling Spatial Autocorrelation in Whole Slide Images to Predict Breast Cancer Outcomes.

.- Image Synthesis and Generative Artificial Intelligence.

.- Augmenting Chest X-ray Datasets with Non-Expert Annotations.

.- Leveraging Synthetic Data for Whole-Body Segmentation in X-ray Images.

.- Transform(AI)ng Radiology with CheXSBT: Integrating Dual-Attention Swin Transformer with BERT for Seamless Chest X-Ray Report Generation.

.- Cardiac Ultrasound Video Generation Using a Diffusion Model with Temporal Transformer.

.- KCLVA: Knowledge-enhanced Contrastive Learning and View-specific Attention for Chest X-ray Report Generation.

.- BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization.

.- MediAug: Exploring Visual Augmentation in Medical Imaging.

.- On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?.

.- DiNO-Diffusion: Scaling Medical Diffusion Models via Self-Supervised Pre-Training.

.- Knowledge-Driven Hypothesis Generation for Burn Diagnosis from Ultrasound with Vision-Language Model.

.- Multimodal Federated Learning With Missing Modalities through Feature Imputation Network.

.- Parameter-Efficient Multimodal Adaptation for Certified Robustness of Medical Vision-Language Models.

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