  Differentiation of colorectal polyps is an important clinical examination. A
computer-aided diagnosis system is required to assist accurate diagnosis from
colonoscopy images. Most previous studies at-tempt to develop models for polyp
differentiation using Narrow-Band Imaging (NBI) or other enhanced images.
However, the wide range of these models' applications for clinical work has
been limited by the lagging of imaging techniques. Thus, we propose a novel
framework based on a teacher-student architecture for the accurate colorectal
polyp classification (CPC) through directly using white-light (WL) colonoscopy
images in the examination. In practice, during training, the auxiliary NBI
images are utilized to train a teacher network and guide the student network to
acquire richer feature representation from WL images. The feature transfer is
realized by domain alignment and contrastive learning. Eventually the final
student network has the ability to extract aligned features from only WL images
to facilitate the CPC task. Besides, we release the first public-available
paired CPC dataset containing WL-NBI pairs for the alignment training.
Quantitative and qualitative evaluation indicates that the proposed method
outperforms the previous methods in CPC, improving the accuracy by 5.6%with
very fast speed.
