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Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings

Eddardaa B. Loussaief, Mohammed Ayad, Domenc Puig, Hatem A. Rashwan

TL;DR

This work tackles domain shift in multi-source MRI segmentation under privacy constraints by proposing a relation-based knowledge distillation framework that unifies Adaptive Affinity Module, Kernel Matrix Module, and Logits Module to transfer both feature relationships and style information from heavy teachers to lightweight students. The method optimizes a combined objective Loss_Total = Loss_Seg + lambda1 Loss_Logits + lambda2 Loss_KM + lambda3 Loss_AA, leveraging gram-matrix-based feature alignment and pairwise pixel affinities to bridge domain gaps. Evaluations on six-site prostate MRI datasets show that lightweight students achieve significant dice-score gains on unseen domains, with competitive efficiency (e.g., $353k$ parameters and $2.2G$ FLOPs) compared to larger teachers. The approach demonstrates practical potential for real-time, privacy-preserving clinical deployment and offers a foundation for extending to other anatomical regions and modalities.

Abstract

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.

Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings

TL;DR

This work tackles domain shift in multi-source MRI segmentation under privacy constraints by proposing a relation-based knowledge distillation framework that unifies Adaptive Affinity Module, Kernel Matrix Module, and Logits Module to transfer both feature relationships and style information from heavy teachers to lightweight students. The method optimizes a combined objective Loss_Total = Loss_Seg + lambda1 Loss_Logits + lambda2 Loss_KM + lambda3 Loss_AA, leveraging gram-matrix-based feature alignment and pairwise pixel affinities to bridge domain gaps. Evaluations on six-site prostate MRI datasets show that lightweight students achieve significant dice-score gains on unseen domains, with competitive efficiency (e.g., parameters and FLOPs) compared to larger teachers. The approach demonstrates practical potential for real-time, privacy-preserving clinical deployment and offers a foundation for extending to other anatomical regions and modalities.

Abstract

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.
Paper Structure (6 sections, 8 equations, 4 figures, 4 tables)

This paper contains 6 sections, 8 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Experiments And Results
  4. Datasets And Implementation Setup
  5. Experimental Evaluation
  6. Conclusion

Figures (4)

  • Figure 1: Overall framework of the proposed generalization method across teacher's and student's networks.
  • Figure 2: The architecture of Adaptive Affinity module (AAM)
  • Figure 3: Segmentation results with Enet distilled from Deeplabv3+ on S4 and S6. The red and green contours denote the GT and predicted mask, respectively.
  • Figure 4: Segmentation Performance of our method. The first row refers to the ESPNet’s prediction and the second row presents results derived from ERFNet Student. .