Authors:
Eddardaa Ben Loussaief
;
Mohammed Ayad
;
Hatem Rashwan
and
Domenec Puig
Affiliation:
Department of Computer Science and Mathematics of Security, University Rovira I Virgili, Tarragona, Spain
Keyword(s):
MRI Segmentation, Adaptive Affinity Module, Kernel Loss, Unseen Generalization.
Abstract:
The joint use of diverse data sources for medical imaging segmentation has emerged as a crucial area of research, aiming to address challenges such as data heterogeneity, domain shift, and data quality discrepancies. Integrating information from multiple data domains has shown promise in improving model generalizabil-ity and adaptability. However, this approach often demands substantial computational resources, hindering its practicality. In response, knowledge distillation (KD) has garnered attention as a solution. KD involves training lightweight models to emulate the behavior of more resource-intensive models, thereby mitigating the computational burden while maintaining performance. This paper addresses the pressing need to develop a lightweight and generalizable model for medical imaging segmentation that can effectively handle data integration challenges. Our proposed approach introduces a novel relation-based knowledge framework by seamlessly combining adaptive affinity-based
and kernel-based distillation. This methodology empowers the student model to accurately replicate the feature representations of the teacher model, facilitating robust performance even in the face of domain shift and data heterogeneity. To validate our approach, we conducted experiments on publicly available multi-source MRI prostate. The results demonstrate a significant enhancement in segmentation performance using lightweight networks. Notably, our method achieves this improvement while reducing both inference time and storage usage.
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