Adaptive Affinity-Based Generalization For MRI Imaging Segmentation
  Across Resource-Limited Settings
Eddardaa B. Loussaief Mohammed Ayad Domenc Puig Hatem A. Rashwan
The joint utilization 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 generalizability 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 light-weight 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 through a gram matrix
that can capture the style representation across features. 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 innovative approach, we
conducted experiments on publicly available multi-source prostate MRI data. 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, rendering it a practical and
efficient solution for real-time medical imaging segmentation.