Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
Journal:
arXiv
Published Date:
May 22, 2025
Abstract
Existing methods for multimodal MRI segmentation with missing modalities
typically assume that all MRI modalities are available during training.
However, in clinical practice, some modalities may be missing due to the
sequential nature of MRI acquisition, leading to performance degradation.
Furthermore, retraining models to accommodate newly available modalities can be
inefficient and may cause overfitting, potentially compromising previously
learned knowledge. To address these challenges, we propose Replay-based
Hypergraph Domain Incremental Learning (ReHyDIL) for brain tumor segmentation
with missing modalities. ReHyDIL leverages Domain Incremental Learning (DIL) to
enable the segmentation model to learn from newly acquired MRI modalities
without forgetting previously learned information. To enhance segmentation
performance across diverse patient scenarios, we introduce the Cross-Patient
Hypergraph Segmentation Network (CHSNet), which utilizes hypergraphs to capture
high-order associations between patients. Additionally, we incorporate
Tversky-Aware Contrastive (TAC) loss to effectively mitigate information
imbalance both across and within different modalities. Extensive experiments on
the BraTS2019 dataset demonstrate that ReHyDIL outperforms state-of-the-art
methods, achieving an improvement of over 2% in the Dice Similarity Coefficient
across various tumor regions.
