论文标题
贴片级实例组歧视与结肠炎评分的借口不变学习
Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring
论文作者
论文摘要
炎症性肠病（IBD），尤其是溃疡性结肠炎（UC），由内镜医生分级，该评估是风险分层和治疗监测的基础。目前，内窥镜表征在很大程度上取决于操作员，导致IBD患者有时不良的临床结果。我们专注于广泛使用但需要可靠地鉴定粘膜炎症变化的蛋黄酱内窥镜评分（MES）系统。大多数现有的深度学习分类方法无法检测到这些细粒度的变化，从而使加州大学的分级成为一项具有挑战性的任务。在这项工作中，我们介绍了一种新颖的贴片级实例组歧视，并使用借口不变的表示学习（PLD-pirl）进行自我监督学习（SSL）。我们的实验表明，与基线监督网络和几种最先进的SSL方法相比，精度和鲁棒性的提高。与基线（RESNET50）监督分类相比，我们提出的PLD-pirl在持有测试数据中获得了4.75％的提高，而在看不见的中心测试数据中，获得了6.64％的速度，以获得TOP-1的准确性。
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.
