arXiv Computer Vision

Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer

arXiv Computer Vision

Sep 10

Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization

arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...

By Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot

computer-vision multimodal

arXiv AI

Jul 16

Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning

arXiv:2607. 13826v1 Announce Type: cross Abstract: Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic vessels on CT imaging, yet expert assessment often shows substantial variability.

By Vincent Ochs, Christoph Kuemmerli, Florentin Bieder, Julia Wolleb, Joel L. Lavanchy, Julia Ruppel, Jan Liechti, Stephanie Taha-Mehlitz, Christian Andreas Nebiker, Beat Mueller, Giuseppe Kito Fusai, Joerg-Matthias Pollok, Anas Taha, Philippe C. Cattin, Sebastian Staubli

rag computer-vision multimodal

arXiv Computer Vision

Aug 25

SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation

arXiv:2608.22532v1 Announce Type: new Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-fr...

By Tal Grossman, Noa Cahan, Hayit Greenspan

diffusion computer-vision fine-tuning

arXiv AI

Aug 11

Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching

arXiv:2608. 08135v1 Announce Type: cross Abstract: Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitations: methods operate on 2D slices or 3D patches rather than whole volumes, and train a separate model for each translation task.

By Daniele Molino, Alessio Zoboli, Camillo Maria Caruso, Valerio Guarrasi, Paolo Soda

diffusion multimodal

arXiv Computer Vision

Aug 26

B-MIM: Biased Masked Image Modeling for Generalizable Segmentation of Fine-Grained Anatomical Structures

arXiv:2608.24364v1 Announce Type: new Abstract: Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic underst...

By Sebasti\'an Gonz\'alez, Karen Sanchez, Jos\'e M. Saavedra, Marcelo Pizarro, Bernard Ghanem

llms computer-vision fine-tuning safety

arXiv Computer Vision

Aug 25

TCSA-UDA: Text-Driven Cross-Semantic Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation

arXiv:2511.05782v3 Announce Type: replace Abstract: Unsupervised domain adaptation (UDA) for medical image segmentation remains challenging due to substantial domain shifts across imaging modalities,...

By Lalit Maurya, Honghai Liu, Reyer Zwiggelaar

llms computer-vision fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 16

CT-VDETR: Semi-supervised 3D Trauma Detection in Computed Tomography (CT) scans using Dense Vertex Relative Position Encoding

arXiv:2603. 12514v2 Announce Type: replace-cross Abstract: Accurate detection and localization of traumatic injuries in abdominal CT remain challenging because voxel-level annotations are limited and expensive to obtain.

By Shivam Chaudhary, Sheethal Bhat, Andreas Maier

llms benchmarks

arXiv Computer Vision

4d ago

Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer

arXiv:2508.11450v2 Announce Type: replace-cross Abstract: Subcortical segmentation in neuroimages plays an important role in understanding brain anatomy and facilitating computer-aided diagnosis of t...

By Augustine X. W. Lee, Pak-Hei Yeung, Jagath C. Rajapakse

computer-vision

arXiv Machine Learning

Jul 22

Local Label-Informed Feature Transfer for Generating Ground-Truth Medical Images: A Comparison of GAN- and Diffusion-Based Approaches

arXiv:2607. 18882v1 Announce Type: cross Abstract: Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features.

By Rick Wilming, Irem Ozseker, Luca Matteo Cornils, Ahc\`ene Boubekki, Benedict Clark, Danny Panknin, Stefan Haufe

diffusion benchmarks

arXiv Computer Vision

Sep 1

On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation

arXiv:2608.29944v1 Announce Type: new Abstract: Brain tumor segmentation in magnetic resonance imaging (MRI) is a critical task for diagnosis and treatment planning. Despite the success of deep learn...

By Henrique Zan Grande, Jo\~ao G. Pitol, Lucas B. Schuck, Rafael V. Serenato, Rayson Laroca, Andre Gustavo Hochuli

computer-vision fine-tuning

arXiv Computer Vision

2d ago

De-GAN - Dynamic Parameter Tuned GAN for 3D Medical Image Segmentation: A Step Towards Generalisation

arXiv:2609.16755v1 Announce Type: new Abstract: Brain tumor segmentation remains difficult because enhancing tumor (ET) has low contrast and overlaps surrounding tissue, while scanner and site variat...

By Zoha Usama, Azadeh Alavi

diffusion computer-vision

arXiv AI

Aug 21

MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation

arXiv:2510. 17529v3 Announce Type: replace-cross Abstract: Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment while monitoring disease progression through serial MRI and clinical follow-up.

By Yovin Yahathugoda, Davide Prezzi, Patricia A. Gutierrez, Piyalitt Ittichaiwong, Vicky Goh, Sebastien Ourselin, Michela Antonelli

llms computer-vision fine-tuning benchmarks

Related stories