Temperature-Driven Robust Disease Detection in Brain and Gastrointestinal Disorders via Context-Aware Adaptive Knowledge Distillation
作者: Saif Ur Rehman Khan, Muhammad Nabeel Asim, Sebastian Vollmer, Andreas Dengel
分类: cs.CV
发布日期: 2025-05-09 (更新: 2025-09-19)
备注: This version v2 updates the title to match the version accepted for publication in biomedical-signal-processing-and-control. The title has been changed to 'Temperature-Driven Robust Disease Detection in Brain and Gastrointestinal Disorders via Context-Aware Adaptive Knowledge Distillation'. The scientific content is unchanged from v1
💡 一句话要点
提出基于温度驱动的知识蒸馏框架以提高脑部和胃肠疾病检测的鲁棒性
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)
关键词: 知识蒸馏 医学影像 蚁群优化 温度调整 疾病检测 深度学习 鲁棒性
📋 核心要点
- 现有知识蒸馏方法在处理医学影像中的不确定性和泛化能力上存在不足，难以适应多样化的医疗条件。
- 本文提出了一种结合蚁群优化和上下文感知的温度缩放方法，以提高知识蒸馏的鲁棒性和适应性。
- 在三个公开基准数据集上进行评估，结果显示该框架在准确率上显著优于现有方法，达到了98.01%的MRI脑肿瘤数据集准确率。
📝 摘要（中文）
医学疾病预测，尤其是通过影像学手段，因医疗数据的复杂性和多变性而面临挑战。尽管近期深度学习模型，特别是知识蒸馏（KD）方法在脑肿瘤影像识别中取得了一定成果，但在处理不确定性和跨多种医疗条件的泛化能力上仍存在局限。为此，本文提出了一种新颖的框架，结合蚁群优化（ACO）进行最佳教师-学生模型选择，并采用上下文感知的温度缩放方法，以实现更稳健的知识转移。实验结果表明，该框架在多个公开基准数据集上显著超越现有最先进的方法，达到了最高的准确率。
🔬 方法详解
问题定义：本文旨在解决现有知识蒸馏方法在医学影像中处理不确定性和泛化能力不足的问题。传统方法依赖于上下文无关的温度参数，无法有效适应医学影像中的多样性和复杂性。
核心思路：提出一种新颖的框架，通过上下文感知的温度调整和蚁群优化选择最佳教师-学生模型，以实现更稳健的知识转移。上下文感知的温度调整依据图像质量、疾病复杂性和教师模型信心等因素进行动态调整。
技术框架：框架主要包括两个模块：1) 上下文感知温度缩放模块，动态调整温度以适应不同的医学影像；2) 蚁群优化模块，从预训练模型中选择最佳的教师-学生模型对。整体流程为：输入医学影像 → 上下文分析 → 温度调整 → 知识蒸馏 → 输出预测结果。
关键创新：最重要的创新在于引入上下文感知的温度调整机制和蚁群优化选择模型，这与传统方法的固定温度参数和简单模型选择方式形成鲜明对比，能够更好地处理复杂的非线性关系。
关键设计：在温度调整中，设计了基于图像质量和模型信心的动态调整策略；在蚁群优化中，采用了多种启发式策略以探索更广泛的解决空间，确保选择最优的教师-学生模型对。损失函数设计上，结合了知识蒸馏和分类损失，以增强模型的学习效果。
📊 实验亮点
实验结果显示，提出的框架在三个公开数据集上均取得了显著的性能提升：在Kaggle脑肿瘤数据集上准确率达到98.01%，在Figshare MRI数据集上为92.81%，在GastroNet数据集上为96.20%。这些结果均超越了现有的基准，证明了该方法的有效性和优越性。
🎯 应用场景
该研究的潜在应用领域包括医学影像分析、疾病早期检测和智能医疗系统。通过提高疾病检测的准确性和鲁棒性，能够为临床决策提供更可靠的支持，进而改善患者的治疗效果和生存率。未来，该框架有望推广至其他医学影像任务和不同类型的疾病检测中。
📄 摘要（原文）
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).