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ORCID29th MIUA 2025: Leeds, UK - Part I
- Sharib Ali
, David C. Hogg, Michelle Peckham:
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, Springer 2026, ISBN 978-3-031-98687-1
Frontiers in Computational Pathology
- Ethar Alzaid, Fayyaz Minhas:
Transductive Survival Ranking for Pan-Cancer Automatic Risk Stratification Using Whole Slide Images. 3-15 - Pablo Meseguer, Rocío del Amor, Valery Naranjo:
Benchmarking Histopathology Foundation Models in a Multi-center Dataset for Skin Cancer Subtyping. 16-28 - Celia Benitez Camacho, Esha Sadia Nasir, Shan E Ahmed Raza:
MitoNet: Efficient Ki-67 Detection in H&E-Stained Images. 29-42 - George Wright, Paul Brighton, Hiroyuki Yoshihara, Joe Thornton, Joanne Muter, Jan Brosens, Fayyaz Minhas:
ASTER: Automated Segmentation of Endometrial Histology Images for Reproductive Health Assessment. 43-57 - Jiaqi Lv, Yijie Zhu, Carmen Guadalupe Colin Tenorio, Brinder Singh Chohan, Mark Eastwood, Shan E Ahmed Raza:
Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images. 58-72 - Moitreya Chaudhuri, Ayantika Das, Keerthi Ram, Mohanasankar Sivaprakasam:
SMatt-DINO: Spatially Aware Masked Attention Network for High Resolution Brain Image Classification. 73-86 - Daniel Brito-Pacheco, Riad Ibadulla, Ximena Fernández, Panos Giannopoulos, Constantino Carlos Reyes-Aldasoro:
Persistent Homology and Gabor Features Reveal Inconsistencies Between Widely Used Colorectal Cancer Training and Testing Datasets. 87-101 - Esha Sadia Nasir, Shan E Ahmed Raza:
SWIFT-Reg: Slide-Wide Intelligent Feature-Based Tissue Registration. 102-117 - Lucan DSilva, Fayyaz Minhas:
Learnable Moran's Index for Modeling Spatial Autocorrelation in Whole Slide Images to Predict Breast Cancer Outcomes. 118-130
Image Synthesis and Generative Artificial Intelligence
- Veronika Cheplygina, Cathrine Damgaard, Trine Naja Eriksen, Dovile Juodelyte, Amelia Jiménez-Sánchez:
Augmenting Chest X-ray Datasets with Non-Expert Annotations. 133-144 - Ahmed Alshenoudy, Bertram Sabrowsky-Hirsch, Stefan Thumfart, Michael Giretzlehner:
Leveraging Synthetic Data for Whole-Body Segmentation in X-Ray Images. 145-158 - Aradhya Khandeparker, Ping Lu:
Transform(AI)ng Radiology with CheXSBT: Integrating Dual-Attention Swin Transformer with BERT for Seamless Chest X-Ray Report Generation. 159-173 - Wenbin Wang, Ping Lu:
Cardiac Ultrasound Video Generation Using a Diffusion Model with Temporal Transformer. 174-186 - Jinlong Zhu, Ping Lu:
KCLVA: Knowledge-Enhanced Contrastive Learning and View-Specific Attention for Chest X-Ray Report Generation. 187-204 - Alejandro Golfe, Natalia P. García-de-la-Puente, Adrián Colomer, Valery Naranjo:
BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization. 205-217 - Xuyin Qi, Zeyu Zhang, Canxuan Gang, Hao Zhang, Lei Zhang, Zhiwei Zhang, Yang Zhao:
MediAug: Exploring Visual Augmentation in Medical Imaging. 218-232 - Raza Imam, Rufael Marew, Mohammad Yaqub:
On the Robustness of Medical Vision-Language Models: Are They Truly Generalizable? 233-256 - Guillermo Jiménez-Pérez, Pedro Osório, Josef Cersovsky, Javier Montalt-Tordera, Jens Hooge, Steffen Vogler, Sadegh Mohammadi:
DiNO-Diffusion: Scaling Medical Diffusion Models via Self-Supervised Pre-Training. 257-274 - Md. Masudur Rahman, Mohamed El Masry, Gayle Gordillo, Juan P. Wachs:
Knowledge-Driven Hypothesis Generation for Burn Diagnosis from Ultrasound with Vision-Language Model. 275-288 - Pranav Poudel, Aavash Chhetri, Prashnna K. Gyawali, Georgios Leontidis, Binod Bhattarai:
Multimodal Federated Learning with Missing Modalities Through Feature Imputation Network. 289-299 - Fahad Shamshad, Noor Hussein, Karthik Nandakumar:
Parameter-Efficient Multimodal Adaptation for Certified Robustness of Medical Vision-Language Models. 300-315
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