[论文解读] Temperature-Driven Robust Disease Detection in Brain and Gastrointestinal Disorders via Context-Aware Adaptive Knowledge Distillation

Saif Ur Rehman Khan, Muhammad Nabeel Asim+2 more|ArXiv.org|May 9, 2025

Brain Tumor Detection and Classification被引用 3

一句话总结

论文提出一种用于知识蒸馏的上下文感知自适应温度缩放，由蚁群优化指导选择教师-学生对，在脑部MRI和GI数据集上实现了最新研究成果的准确性。

ABSTRACT

Medical disease prediction, particularly through imaging, remains a challenging task due to the complexity and variability of medical data, including noise, ambiguity, and differing image quality. Recent deep learning models, including Knowledge Distillation (KD) methods, have shown promising results in brain tumor image identification but still face limitations in handling uncertainty and generalizing across diverse medical conditions. Traditional KD methods often rely on a context-unaware temperature parameter to soften teacher model predictions, which does not adapt effectively to varying uncertainty levels present in medical images. To address this issue, we propose a novel framework that integrates Ant Colony Optimization (ACO) for optimal teacher-student model selection and a novel context-aware predictor approach for temperature scaling. The proposed context-aware framework adjusts the temperature based on factors such as image quality, disease complexity, and teacher model confidence, allowing for more robust knowledge transfer. Additionally, ACO efficiently selects the most appropriate teacher-student model pair from a set of pre-trained models, outperforming current optimization methods by exploring a broader solution space and better handling complex, non-linear relationships within the data. The proposed framework is evaluated using three publicly available benchmark datasets, each corresponding to a distinct medical imaging task. The results demonstrate that the proposed framework significantly outperforms current state-of-the-art methods, achieving top accuracy rates: 98.01% on the MRI brain tumor (Kaggle) dataset, 92.81% on the Figshare MRI dataset, and 96.20% on the GastroNet dataset. This enhanced performance is further evidenced by the improved results, surpassing existing benchmarks of 97.24% (Kaggle), 91.43% (Figshare), and 95.00% (GastroNet).

研究动机与目标

• 通过使知识蒸馏自适应于不确定性与数据质量来提高医疗影像疾病检测的鲁棒性与准确性。
• 自动使用蚁群优化从教师-学生模型池中选择最优对，以提升蒸馏效率。
• 在脑部MRI和胃肠成像基准上展示相较现有方法的性能提升。

提出的方法

• 引入蚁群优化从预训练网络池中选择最佳教师-学生模型对。
• 开发一个上下文感知、基于规则的自适应温度缩放用于KD，根据图像质量、疾病复杂度和教师信心调整温度。
• 在KD框架中使用DenseNet201作为教师和ResNet152V2作为学生。
• 设定上下文感知规则（规则1至规则3）以在训练过程中调节蒸馏温度。
• 将自适应温度定义为tau(x)=1+alpha*U(x)，其中U(x)是一个不确定性分数。
• 在三个公开数据集上进行评估，包括Kaggle脑MRI脑肿瘤、Figshare MRI和GastroNet。

实验结果

研究问题

• RQ1 上下文感知自适应温度缩放是否能提升不确定医疗影像的KD迁移效果？
• RQ2 蚁群优化是否能在医疗影像KD中有效选择更优秀的教师-学生模型对？
• RQ3 与现有方法相比，所提框架在脑部MRI和胃肠成像基准上的性能提升有多大？

主要发现

• 在Kaggle脑肿瘤数据集上达到98.01%准确率。
• 在Figshare MRI数据集上达到92.81%准确率。
• 在GastroNet数据集上达到96.20%准确率。
• 超越基准97.24%（Kaggle）、91.43%（Figshare）和95.00%（GastroNet）。
• 上下文感知的温度缩放提高了对不确定性和噪声的鲁棒性。
• 蚁群优化高效地从16个预训练模型中选择了最佳教师-学生模型对。