  Colorectal cancer, largely arising from precursor lesions called polyps,
remains one of the leading causes of cancer-related death worldwide. Current
clinical standards require the resection and histopathological analysis of
polyps due to test accuracy and sensitivity of optical biopsy methods falling
substantially below recommended levels. In this study, we design a novel
capsule network architecture (D-Caps) to improve the viability of optical
biopsy of colorectal polyps. Our proposed method introduces several technical
novelties including a novel capsule architecture with a capsule-average pooling
(CAP) method to improve efficiency in large-scale image classification. We
demonstrate improved results over the previous state-of-the-art convolutional
neural network (CNN) approach by as much as 43%. This work provides an
important benchmark on the new Mayo Polyp dataset, a significantly more
challenging and larger dataset than previous polyp studies, with results
stratified across all available categories, imaging devices and modalities, and
focus modes to promote future direction into AI-driven colorectal cancer
screening systems. Code is publicly available at
https://github.com/lalonderodney/D-Caps .
