Deep Self-knowledge Distillation: A hierarchical supervised learning for coronary artery segmentation

• URL: http://arxiv.org/abs/2509.03173v1
• Date: Wed, 03 Sep 2025 09:44:11 GMT
• Title: Deep Self-knowledge Distillation: A hierarchical supervised learning for coronary artery segmentation
• Authors: Mingfeng Lin,
• Abstract summary: This paper introduces Deep Self-knowledge Distillation, a novel approach for coronary artery segmentation.<n>By combining Deep Distribution Loss and Pixel-wise Self-knowledge Distillation Loss, our method enhances the student model's segmentation performance.<n>Our approach outperforms the dice coefficient, accuracy, sensitivity and IoU compared to other models in comparative evaluations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coronary artery disease is a leading cause of mortality, underscoring the critical importance of precise diagnosis through X-ray angiography. Manual coronary artery segmentation from these images is time-consuming and inefficient, prompting the development of automated models. However, existing methods, whether rule-based or deep learning models, struggle with issues like poor performance and limited generalizability. Moreover, current knowledge distillation methods applied in this field have not fully exploited the hierarchical knowledge of the model, leading to certain information waste and insufficient enhancement of the model's performance capabilities for segmentation tasks. To address these issues, this paper introduces Deep Self-knowledge Distillation, a novel approach for coronary artery segmentation that leverages hierarchical outputs for supervision. By combining Deep Distribution Loss and Pixel-wise Self-knowledge Distillation Loss, our method enhances the student model's segmentation performance through a hierarchical learning strategy, effectively transferring knowledge from the teacher model. Our method combines a loosely constrained probabilistic distribution vector with tightly constrained pixel-wise supervision, providing dual regularization for the segmentation model while also enhancing its generalization and robustness. Extensive experiments on XCAD and DCA1 datasets demonstrate that our approach outperforms the dice coefficient, accuracy, sensitivity and IoU compared to other models in comparative evaluations.

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