Polyp detection in Colonoscopy Videos Using Deeply-Learned Hierarchical Features

This paper summarizes the method of polyp detection in colonoscopy images and provides preliminary results to participate in ISBI 2015 Grand Challenge on Automatic Polyp Detection in Colonoscopy videos. The key aspect of the proposed method is to learn hierarchical features using convolutional neural network. The features are learned in different scales to provide scale-invariant features through the convolutional neural network, and then each pixel in the colonoscopy image is classified as polyp pixel or non-polyp pixel through fully connected network. The result is refined via smooth filtering and thresholding step. Experimental result shows that the proposed neural network can classify patches of polyp and non-polyp region with an accuracy of about 90%. Key Words: Convolutional neural network, hierarchical feature learning. 1 Introduction of Research Team Our group, Machine Intelligence and Pattern Analysis Laboratory, aims to solve computer vision and machine learning problems. Recently, our interest lies within deep learning algorithms and its applications, especially the ones related with medical imaging. Although this is the first time we deal with endoscopy image analysis, we previously conducted research on automatic Bosniak classification problem from renal CT scan images