Abstract
The detection and classification of anatomies from medical images has traditionally been developed in a two-stage process, where the first stage detects the regions of interest (ROIs), while the second stage classifies the detected ROIs. Recent developments from the computer vision community allowed the unification of these two stages into a single detection and classification model that is trained in an end to end fashion. This allows for a simpler and faster training and inference procedures because only one model (instead of the two models needed for the two-stage approach) is required. In this paper, we adapt a recently proposed onestage detection and classification approach for the new 5class polyp classification problem. We show that this onestage approach is not only competitive in terms of detection and classification accuracy with respect to the two-stage approach, but it is also substantially faster for training and testing. We also show that the one-stage approach produces competitive detection results compared to the state of the art results on the MICCAI 2015 polyp detection challenge.