Explainable HCC Diagnosis on Dynamic Contrast-Enhanced MRI with a Li-RADS Concept Bottleneck
Abstract
We propose an explainable end-to-end framework for hepatocellular carcinoma (HCC) diagnosis on dynamic contrast-enhanced (DCE) liver MRI. Our method embeds Liver Imaging Reporting and Data System (Li-RADS)–inspired concepts into the network via a multi-head concept bottleneck. A 2.5D EfficientNet backbone processes lesion-centred multiphase MRI crops, and a 4-head architecture jointly predicts continuous soft labels for non-rim arterial phase hyperenhancement (APHE), portal venous/delayed washout and capsule, lesion morphology, and a LR-5 score (definite HCC vs non-HCC) based on the Li-RADS guidelines. Soft labels are derived automatically from intra-lesional, peri-lesional and parenchymal intensity patterns, and the network is trained with uncertainty-weighted losses to balance concept prediction, contrast regression and HCC classification. On our cohort, the Li-RADS–inspired bottleneck substantially improves NormGrad explanation accuracy, geometric stability and intensity robustness while maintaining PR AUC comparable to a single-head baseline, highlighting an interpretable alternative to a black-box HCC classifier.
Cite
Text
Monnin et al. "Explainable HCC Diagnosis on Dynamic Contrast-Enhanced MRI with a Li-RADS Concept Bottleneck." Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, 2026.Markdown
[Monnin et al. "Explainable HCC Diagnosis on Dynamic Contrast-Enhanced MRI with a Li-RADS Concept Bottleneck." Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, 2026.](https://mlanthology.org/midl/2026/monnin2026midl-explainable/)BibTeX
@inproceedings{monnin2026midl-explainable,
title = {{Explainable HCC Diagnosis on Dynamic Contrast-Enhanced MRI with a Li-RADS Concept Bottleneck}},
author = {Monnin, Killian and Jeltsch, Patrick and Fernandes-Mendes, Lucia and Cazzagon, Vasco and Yüce, Murat and Yadav, Vivek and Jreige, Mario and Gulizia, Marianna and Christinet, Montserrat Fraga and Girardet, Raphaël and Dromain, Clarisse and Taouli, Bachir and Vietti-Violi, Naïk and Richiardi, Jonas},
booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning},
year = {2026},
pages = {3283-3313},
volume = {315},
url = {https://mlanthology.org/midl/2026/monnin2026midl-explainable/}
}