用深度学习预测胶囊肠镜图像清洁度，模型在高稀疏下仍保持88%准确率。
Using deep learning for predicting cleansing quality of colon capsule endoscopy images
- 基于ResNet-18与分层交叉验证，结合结构化剪枝提升效率
- 剪枝后达79%稀疏度，跨验证准确率仍为88%
- 通过多种CAM方法增强可解释性，适合临床决策支持
本研究探索深度学习在结肠胶囊内镜（CCE）图像清洁度预测中的应用。基于500张由14名临床医生按Leighton-Rex量表标注的图像，采用ResNet-18模型进行分类，并通过分层K折交叉验证确保性能稳健。通过迭代式结构化剪枝，在保持高准确率的前提下实现显著稀疏性。使用Grad-CAM、Grad-CAM++、Eigen-CAM、Ablation-CAM和Random-CAM评估剪枝模型的可解释性，采用ROAD方法进行一致性评估。结果表明，剪枝模型在79%稀疏度下仍可达到88%的交叉验证准确率，相比未剪枝模型（84%准确率）效率显著提升。研究还指出评估CCE图像清洁度的挑战，强调临床应用中可解释性的重要性，并讨论了使用ROAD方法的局限性。最后，通过自适应温度缩放变体对剪枝模型进行外部数据集校准。
原文摘要 · Abstract (English)
In this study, we explore the application of deep learning techniques for predicting cleansing quality in colon capsule endoscopy (CCE) images. Using a dataset of 500 images labeled by 14 clinicians on the Leighton-Rex scale (Poor, Fair, Good, and Excellent), a ResNet-18 model was trained for classification, leveraging stratified K-fold cross-validation to ensure robust performance. To optimize the model, structured pruning techniques were applied iteratively, achieving significant sparsity while maintaining high accuracy. Explainability of the pruned model was evaluated using Grad-CAM, Grad-CAM++, Eigen-CAM, Ablation-CAM, and Random-CAM, with the ROAD method employed for consistent evaluation. Our results indicate that for a pruned model, we can achieve a cross-validation accuracy of 88% with 79% sparsity, demonstrating the effectiveness of pruning in improving efficiency from 84% without compromising performance. We also highlight the challenges of evaluating cleansing quality of CCE images, emphasize the importance of explainability in clinical applications, and discuss the challenges associated with using the ROAD method for our task. Finally, we employ a variant of adaptive temperature scaling to calibrate the pruned models for an external dataset.
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