Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is graded by endoscopists and this assessment is the basis for risk stratification and therapy monitoring. Presently, endoscopic characterisation is largely operator dependant leading to sometimes undesirable clinical outcomes for patients with IBD. We focus on the Mayo Endoscopic Scoring (MES) system which is widely used but requires the reliable identification of subtle changes in mucosal inflammation. Most existing deep learning classification methods cannot detect these fine-grained changes which make UC grading such a challenging task. In this work, we introduce a novel patch-level instance-group discrimination with pretext-invariant representation learning (PLD-PIRL) for self-supervised learning (SSL). Our experiments demonstrate both improved accuracy and robustness compared to the baseline supervised network and several state-of-the-art SSL methods. Compared to the baseline (ResNet50) supervised classification our proposed PLD-PIRL obtained an improvement of 4.75% on hold-out test data and 6.64% on unseen center test data for top-1 accuracy.
翻译:肠胃炎(IBD),特别是肺炎(UC),由内科医生定级,这一评估是风险分级和治疗监测的基础。目前,内分层特征主要依赖操作,导致IBD病人有时不受欢迎的临床结果。我们着重研究广泛使用的Mayo Endoscopic Scoring(MES)系统,但需要可靠地查明肌肉炎的细微变化。大多数现有的深层次学习分类方法无法检测这些细微的细微变化,使得UC的分级成为一项具有挑战性的任务。在这项工作中,我们采用了新的补丁类实例歧视,为自我监督学习采用隐性代理学习(PLD-PIRL)。我们的实验表明,与基准监督网络和若干最先进的SSL方法相比,准确性提高了准确性。与基线(ResNet50)相比,我们提议的PLD-PIRL监督分类,我们提议的PL在搁置测试数据上改进了4.75%,在秘密中心测试数据上改进了6.4%。