Abstract
We developed a deep learning classifier of rectal cancer response (tumor vs. no-tumor) to total neoadjuvant treatment (TNT) from endoscopic images acquired before, during, and following TNT. We further evaluated the network's ability in a near out-of-distribution (OOD) problem to identify local regrowth (LR) from follow-up endoscopy images acquired several months to years after completing TNT. We addressed endoscopic image variability by using optimal mass transport-based image harmonization. We evaluated multiple training regularization schemes to study the ResNet-50 network's in-distribution and near-OOD generalization ability. Test time augmentation resulted in the most considerable accuracy improvement. Image harmonization resulted in slight accuracy improvement for the near-OOD cases. Our results suggest that off-the-shelf deep learning classifiers can detect rectal cancer from endoscopic images at various stages of therapy for surveillance.
Abstract (translated)
我们开发了一个用于直肠癌反应分层的深度学习分类器（肿瘤与无肿瘤）来预测从结镜图像中获取的初始、进行中和和完成中和治疗的肿瘤反应（TNT）。我们还评估了网络在近分布外（OOD）问题中识别局部生长（LR）的能力，从完成TNT数月到数年后的随访结镜图像中。我们通过使用最优质量传输图像和谐来解决结镜图像变异性。我们评估了多个训练正则化方案，以研究ResNet-50网络在分布内和近分布外的泛化能力。测试时间增强导致准确性提高最为显著。图像和谐在近分布外案例中略微提高了一些准确性。我们的结果表明，标准的深度学习分类器可以从结镜图像中检测直肠癌的不同治疗阶段。
URL
https://arxiv.org/abs/2405.03762
