Dual-Model Weight Selection and Self-Knowledge Distillation for Medical Image Classification

• URL: http://arxiv.org/abs/2508.20461v1
• Date: Thu, 28 Aug 2025 06:15:06 GMT
• Title: Dual-Model Weight Selection and Self-Knowledge Distillation for Medical Image Classification
• Authors: Ayaka Tsutsumi, Guang Li, Ren Togo, Takahiro Ogawa, Satoshi Kondo, Miki Haseyama,
• Abstract summary: We propose a novel medical image classification method that integrates dual-model weight selection with self-knowledge distillation (SKD)<n>Our method overcomes the limitations of conventional approaches, which often fail to retain critical information in compact models.
• Score: 47.17249726328169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel medical image classification method that integrates dual-model weight selection with self-knowledge distillation (SKD). In real-world medical settings, deploying large-scale models is often limited by computational resource constraints, which pose significant challenges for their practical implementation. Thus, developing lightweight models that achieve comparable performance to large-scale models while maintaining computational efficiency is crucial. To address this, we employ a dual-model weight selection strategy that initializes two lightweight models with weights derived from a large pretrained model, enabling effective knowledge transfer. Next, SKD is applied to these selected models, allowing the use of a broad range of initial weight configurations without imposing additional excessive computational cost, followed by fine-tuning for the target classification tasks. By combining dual-model weight selection with self-knowledge distillation, our method overcomes the limitations of conventional approaches, which often fail to retain critical information in compact models. Extensive experiments on publicly available datasets-chest X-ray images, lung computed tomography scans, and brain magnetic resonance imaging scans-demonstrate the superior performance and robustness of our approach compared to existing methods.

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