MANIFOLD-CONSTRAINED DISTILLATION FOR MISSING-MODALITY BRAIN TUMOR SEGMENTATION

Magnetic Resonance Imaging (MRI)-based brain tumor segmentation is crucial for clinical diagnosis and treatment planning, yet its accuracy degrades severely when part of the imaging modalities is missing. Knowledge distillation (KD) alleviates this by using a full-modality teacher to guide a modality-constrained student. Existing methods mainly rely on feature-level distillation, which incurs additional memory and computational costs, especially for high-resolution med- ical images. In contrast, logit-only distillation provides a lightweight alternative without explicit feature alignment, yet remains unexplored in missing-modality brain tumor segmentation. However, native logit distillation may force the student toward teacher predictions outside its reachable prediction space, as the teacher exploits modality information unavailable to the student. In this paper, we refor- mulate logit distillation under missing modalities as a manifold-constrained tar- get estimation problem, and propose Manifold-Constrained Distillation (MCD), a novel manifold-aware logit distillation framework. MCD projects the teacher’s correction onto the student’s transferable space under the Fisher geometry, re- taining only the realizable component as the target. On BraTS 2018 and BraTS 2024 benchmarks, MCD consistently improves segmentation accuracy over strong baselines across diverse missing-modality scenarios. To further demonstrate the generalizability of our framework, we extend MCD to an additional task, multi- modal sentiment analysis, and again observe the superiority of our MCD model.