Adaptive weighted multi-teacher distillation for efficient medical imaging segmentation with limited data

Identificador:  imarina:9452339

Handle: https://hdl.handle.net/20.500.11797/imarina9452339

Autors:  Ben Loussaief, E; Rashwan, HA; Ayad, M; Khalid, A; Puig, D

Resum:
Advances in deep learning models have significantly improved performance in medical tasks, but their complex structures and high computational requirements pose challenges for clinical implementation. Additionally, data privacy concerns limit the availability of comprehensive datasets needed to train accurate models. To address these issues, we propose a novel adaptive knowledge distillation (KD) framework for medical imaging segmentation that integrates intermediate and high-level feature pairwise relationships between teacher and student models. Our framework features adaptive multi-teacher distillation, where multiple teacher models, each trained on limited data from different sites and hospitals with various scanning protocols, distill their knowledge to a student model using adaptive weighting. This method allows each teacher to convey deep feature representations to the student's intermediate layers, enhancing performance without increasing complexity. To validate the efficacy of our framework, we conducted extensive experiments on two publicly available medical datasets, focusing on prostate and spleen tumor segmentation tasks. Our adaptive KD approach significantly improved dice scores by up to 9%, surpassing all tested baseline models. These results highlight the potential of our KD framework to enhance medical imaging segmentation while ensuring data privacy and security.