No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation
Researchers have developed a new framework called AdaMM to improve brain tumor segmentation using multi-modal MRI data, even when some modalities are missing. This approach utilizes knowledge distillation and adaptive refinement modules to enhance the model's ability to handle incomplete inputs. Experiments on benchmark datasets show AdaMM outperforms existing methods, particularly in scenarios with single or limited modalities, offering practical guidance for future research. AI
IMPACT Enhances robustness of AI models in medical imaging for scenarios with incomplete data.