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
翻译:肠胃癌的发病率每年都在惊人地增长,导致死亡率大幅上升。内分层检测正在提供关键的诊断支持,然而,GI上下部的细微损伤很难检测,并导致大量误测。在这项工作中,我们利用深层学习开发一个框架,以改善难以检测损伤的局部化,并最大限度地减少误测率。我们建议结束师生学习结构,即使用一个具有较大数据集的班级受过训练的教师模型的等级概率来惩罚多级学生网络。我们的模型在内分层疾病检测(EDD202020挑战)和Kvasir-SEG数据集的平均精确度方面表现较高。此外,我们表明,利用这种学习模式,我们的模式可以被普遍接受到看不见的测试集,为临床关键肿瘤和聚变异类别提供较高的AP。