Uncertainty quantification in 3D deep learning for detecting serrated polyps in CT colonography

Abstract

Serrated polyps have emerged as a new important target lesion in colorectal cancer screening. Although artificial intelligence (AI) can be used to detect serrated polyps at a high accuracy in CT colonography, it is also important to understand the uncertainties regarding the decisions made by the AI. In this pilot study, we explored the quantification of the uncertainty in 3D deep learning for the detection of serrated polyps in CT colonography. The uncertainty was estimated by use of a Monte-Carlo dropout method, and quantified by characterizing the variance of the predictions made on the Monte-Carlo samples. For a preliminary evaluation, we performed a 10-fold per-patient cross-validation to compare the accuracies and uncertainties of the detections made by the two 3D DenseNet models that we previously identified as having a high performance in the detection of serrated polyps. The materials included 94 clinical CT colonography cases with biopsy-confirmed serrated polyps. Our preliminary results indicate that both 3D DenseNets were able to detect serrated polyps at a high accuracy and high certainty. However, the 3D DenseNet with a larger number of input convolutions yielded more consistent certainties in the detection of different clinical pathologies of polyps than did the DenseNet with a smaller number of input convolutions. Our results indicate that the uncertainty quantification can provide constructive quantitative insights regarding the quality of the detections made by AI, and that serrated polyps can be detected automatically in CT colonography not only at a high detection accuracy but also at a high certainty.