Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation

• URL: http://arxiv.org/abs/2509.16436v1
• Date: Fri, 19 Sep 2025 21:31:05 GMT
• Title: Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation
• Authors: Zhejia Zhang, Junjie Wang, Le Zhang,
• Abstract summary: We propose a multimodal MRI classification model based on the mmFormer architecture with an adaptive module for handling arbitrary combinations of missing modalities.<n>This method is evaluated on the test set of Comprehensive Analysis & Computing of REal-world medical images (CARE 2025 challenge)<n>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.
• Score: 8.687370165870613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities [41.8469011437549]
  Predicting survival outcomes for non-small cell lung cancer (NSCLC) patients is challenging due to the different individual prognostic features.<n>State-of-the-art models rely on available data to create patient-level representations or use generative models to infer missing modalities.<n>We propose a Multimodal Contrastive Variational AutoEncoder (MCVAE) to address this issue.
  arXiv  Detail & Related papers  (2026-02-19T14:29:34Z)
• Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI [5.332404648315838]
  We study ischemic stroke lesion segmentation using multimodal diffusion MRI from the ISLES 2022 dataset.<n>Several state-of-the-art convolutional and transformer-based architectures, including U-Net variants, Swin-UNet, and TransUNet, are benchmarked.<n>Results show that transformer-based models outperform convolutional baselines, and the proposed dual-encoder TransUNet achieves the best performance, reaching a Dice score of 85.4% on the test set.
  arXiv  Detail & Related papers  (2025-12-23T15:24:31Z)
• SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRI [0.0]
  Brain MRI scans are often found in four modalities, consisting of T1-weighted with and without contrast enhancement (T1ce and T1w), T2-weighted imaging (T2w) and Flair.<n>We propose SLaM-DiMM, a novel missing modality generation framework that harnesses the power of diffusion models to synthesize any of the four target MRI modalities from other available modalities.
  arXiv  Detail & Related papers  (2025-09-19T14:27:35Z)
• DeSamba: Decoupled Spectral Adaptive Framework for 3D Multi-Sequence MRI Lesion Classification [0.6749750044497732]
  DeSamba is a framework designed to extract decoupled representations and adaptively fuse spatial and spectral features for lesion classification.<n>DeSamba achieves 62.10% Top-1 accuracy, 63.62% F1-score, 87.71% AUC, and 93.55% Top-3 accuracy on an external validation set.
  arXiv  Detail & Related papers  (2025-07-21T10:42:21Z)
• Graph-based Multi-Modal Interaction Lightweight Network for Brain Tumor Segmentation (GMLN-BTS) in Edge Iterative MRI Lesion Localization System (EdgeIMLocSys) [6.451534509235736]
  We propose the Edge Iterative MRI Lesion Localization System (EdgeIMLocSys), which integrates Continuous Learning from Human Feedback.<n>Central to this system is the Graph-based Multi-Modal Interaction Lightweight Network for Brain Tumor (GMLN-BTS)<n>Our proposed GMLN-BTS model achieves a Dice score of 85.1% on the BraTS 2017 dataset with only 4.58 million parameters, representing a 98% reduction compared to mainstream 3D Transformer models.
  arXiv  Detail & Related papers  (2025-07-14T07:29:49Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading [47.50733518140625]
  Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly. We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
  arXiv  Detail & Related papers  (2024-01-17T07:54:49Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation [75.58395328700821]
  We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
  arXiv  Detail & Related papers  (2021-07-07T19:06:34Z)
• Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis [8.363448006582065]
  We propose a deep learning based multi-modal computing model for MRI synthesis with feature disentanglement strategy. The proposed approach decomposes each input modality into modality-invariant space with shared information and modality-specific space with specific information. To address the lack of specific information of the target modality in the test phase, a local adaptive fusion (LAF) module is adopted to generate a modality-like pseudo-target.
  arXiv  Detail & Related papers  (2021-05-06T17:22:22Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.