# Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation

arXiv: https://arxiv.org/abs/2509.16436 · HTML: https://mlsanity.com/paper/2509.16436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation](https://mlsanity.com/paper/2206.02425/) (2022)
- [Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modality](https://mlsanity.com/paper/2310.01035/) (2023)
- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging](https://mlsanity.com/paper/2306.12054/) (2023)
- [MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging](https://mlsanity.com/paper/2405.02918/) (2024)
- [An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images](https://mlsanity.com/paper/2212.06265/) (2022)

## Abstract (arXiv)

In real-world clinical settings, magnetic resonance imaging (MRI) frequently suffers from missing modalities due to equipment variability or patient cooperation issues, which can significantly affect model performance. To address this issue, we propose a multimodal MRI classification model based on the mmFormer architecture with an adaptive module for handling arbitrary combinations of missing modalities. Specifically, this model retains the hybrid modality-specific encoders and the modality-correlated encoder from mmFormer to extract consistent lesion features across available modalities. In addition, we integrate a missing-modality compensation module which leverages zero-padding, modality availability masks, and a Delta Function with learnable statistical parameters to dynamically synthesize proxy features for recovering missing information. To further improve prediction performance, we adopt a cross-validation ensemble strategy by training multiple models on different folds and applying soft voting during inference. This method is evaluated on the test set of Comprehensive Analysis & Computing of REal-world medical images (CARE) 2025 challenge, targeting the Liver Fibrosis Staging (LiFS) task based on non-contrast dynamic MRI scans including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). For Cirrhosis Detection and Substantial Fibrosis Detection on in-distribution vendors, our model obtains accuracies of 66.67%, and 74.17%, and corresponding area under the curve (AUC) scores of 71.73% and 68.48%, respectively.