论文标题
知识蒸馏，具有内窥镜疾病检测的班级感知损失
Knowledge distillation with a class-aware loss for endoscopic disease detection
论文作者
论文摘要
胃肠道（GI）癌症的患病率每年令人震惊，导致死亡率大幅上升。内窥镜检测提供了至关重要的诊断支持，但是，上gi中和下gi中的细微病变很难检测到，并引起大量未检测。在这项工作中，我们利用深度学习来开发一个框架，以改善难以检测病变的本地化并最大程度地减少遗漏的检测率。我们提出了一个端到端的学生教师学习设置，其中使用较大数据集的一个班级训练有素的教师模型的班级概率用于惩罚多级学生网络。我们的模型在两种内窥镜疾病检测（EDD2020）挑战和Kvasir-SEG数据集上的平均平均精度（MAP）方面达到了更高的性能。此外，我们表明，使用这种学习范式，我们的模型可以推广到看不见的测试集，从而为临床上关键的肿瘤和息肉类别提供更高的APS
Prevalence of gastrointestinal (GI) cancer is growing alarmingly every year leading to a substantial increase in the mortality rate. Endoscopic detection is providing crucial diagnostic support, however, subtle lesions in upper and lower GI are quite hard to detect and cause considerable missed detection. In this work, we leverage deep learning to develop a framework to improve the localization of difficult to detect lesions and minimize the missed detection rate. We propose an end to end student-teacher learning setup where class probabilities of a trained teacher model on one class with larger dataset are used to penalize multi-class student network. Our model achieves higher performance in terms of mean average precision (mAP) on both endoscopic disease detection (EDD2020) challenge and Kvasir-SEG datasets. Additionally, we show that using such learning paradigm, our model is generalizable to unseen test set giving higher APs for clinically crucial neoplastic and polyp categories
