Abstract
Angiodysplasia is a small primary lesion in the gut, which may cause gastrointestinal bleeding. Wireless capsule endoscopy is one of the best tools to capture images of these lesions. Since it generates thousands of images, it is crucial to segment angiodysplasia automatically. Recently, AlbuNet, a deep learning network, has shown a promising result and considered as the state-of-the-art technique. In this paper, we aim to enhance AlbuNet from two angles. First, squeeze-and-excitation is similar to the concept of attention on different channels, so it can combine variants of extracted features. Second, a pre-processing step to enhance an image’s quality is proposed by applying a computer vision technique called “contrast limit adaptive histogram equalization (CLAHE)”. The experiment was conducted on two benchmarks: MICCAI 2017 and 2018 datasets and evaluated in terms of Dice coefficient and Jaccard index scores. The results showed that our model outperformed a baseline technique, AlbuNet, on both datasets.
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Gobpradit, S., Vateekul, P. (2020). Angiodysplasia Segmentation on Capsule Endoscopy Images Using AlbuNet with Squeeze-and-Excitation Blocks. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_25
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