CirrGuide: A Deep Cascaded Framework for Liver Cirrhosis Segmentation and Severity Classification from T2-Weighted MRI
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2610.09784v1 Announce Type: new Abstract: Reliable liver segmentation in contrast-enhanced MRI is essential for quantitative hepatic assessment, treatment planning, and longitudinal disease mon...
DINO-Med introduces a patch‑based framework that adapts natural‑image foundation models, specifically DINOv3, to multi‑modal medical imaging. The method uses training‑free registration, automated localization, and mask‑filtered patch extraction to aggregate patch‑level features into subject‑level diagnostics. In liver fibrosis staging, DINOv3 outperforms handcrafted radiomics, ResNet, and SAM‑Med2D features, achieving 78.4% accuracy for mild fibrosis (S1) and 75.8% for cirrhosis (S4) on the CARE 2025 cohort.
arXiv:2610.18243v1 Announce Type: cross Abstract: Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportun...
LiFUSE introduces a unified frequency‑spatial self‑supervised pretraining framework for liver disease analysis on non‑contrast CT scans. The method employs a frequency branch that uses random band‑stop masking and spectrum reconstruction, and a spatial branch that performs hybrid‑granularity masked volumetric reconstruction, with a cross‑domain consistency scheme to fuse the two branches. Experiments on three clinical tasks and a public transfer setting demonstrate that LiFUSE consistently outperforms strong baselines, indicating its potential to enhance liver disease screening using low‑contrast NCCT images.
The paper introduces DINO-Med, a patch‑based framework that adapts natural‑image foundation models to multi‑modal medical imaging, specifically for liver fibrosis staging. It processes raw multimodal scans through training‑free registration, automated localization, and mask‑filtered patch extraction, then aggregates patch‑level insights into subject‑level diagnostics. Using the CARE 2025 Liver Track 4 cohort, the DINOv3‑based approach achieved the highest classification accuracy (78.4% for mild fibrosis and 75.8% for cirrhosis) compared to other feature representations.
arXiv:2508. 12410v3 Announce Type: replace-cross Abstract: Liver cirrhosis plays a critical role in the prognosis of chronic liver disease.