Medical Image Understanding and Analysis

29th Annual Conference, MIUA 2025, Leeds, UK, July 15-17, 2025, Proceedings, Part I
Herausgegeben:Ali, Sharib; Hogg, David; Peckham, Michelle

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 15916
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-98687-1
• Seitenzahl: 310
• Erscheinungstermin: 15. August 2025
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9783031986871
• Artikelnr.: 74384919

Inhaltsangabe

.- 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.