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.

翻译：本研究探讨了深度学习技术在预测结肠胶囊内镜图像清洁质量方面的应用。我们使用包含500张图像的数据集（由14位临床医生按照Leighton-Rex量表标注为差、一般、好、优秀四个等级），通过分层K折交叉验证训练ResNet-18分类模型以确保稳健性能。为优化模型，我们采用结构化剪枝技术进行迭代处理，在保持高精度的同时实现了显著稀疏化。使用Grad-CAM、Grad-CAM++、Eigen-CAM、Ablation-CAM和Random-CAM方法评估剪枝模型的可解释性，并采用ROAD方法进行一致性评估。结果表明，剪枝模型在实现79%稀疏度的同时可获得88%的交叉验证准确率，证明剪枝能在保持84%基准性能的前提下有效提升模型效率。我们同时指出评估结肠胶囊内镜图像清洁质量面临的挑战，强调可解释性在临床应用中的重要性，并讨论了ROAD方法在本任务中的适用性问题。最后，我们采用自适应温度缩放的变体方法对剪枝模型进行外部数据集的校准。