Hetero-modal learning and corruption-resistant hetero-modal inference for joint segmentation of white matter hyperintensities and ischaemic stroke lesions in MRI
White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are visually confounding, co-occurring pathologies that require large, diverse datasets for robust deep learning segmentation. However, assembling such datasets is hindered by cohort samples that lack reference segmentations from both features and complete sets of MRI structural sequences (i.e., "modalities"). To maximise data utility, we investigate hetero-modal learning using a dataset of 206 vascular disease patients across four MRI sequences (T1-weighted, T2-weighted, fluid-attenuated inversion recovery, and diffusion-weighted imaging) with expert annotations of both WMH and ISL. We demonstrate that hetero-modal learning outperforms models trained using a single imaging modality in scenarios with substantial missing data, including a split where only 10% of the training data contains all four modalities while the remainder is uni-modal, and a split relying on a single shared "anchor" modality with zero overlap between the remaining modalities. Furthermore, models trained under this second split successfully perform inference on unseen combinations of modalities. Yet, while standard hetero-modal networks handle missing sequences, clinical deployment introduces the additional challenge of silent data degradation - where modalities are present but severely corrupted. To bridge this gap, we introduce the Multimodal Attention Router (MMAR) block. Our experiments demonstrate that, when trained with a "corruption augmentation" strategy, the MMAR effectively dynamically weights the encoded features of each modality, maintaining strong performance during hetero-modal inference even in the presence of unflagged catastrophically corrupted modalities.