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Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays by Han, Yan; Chen, Chongyan; Tang, Liyan; Lin, Mingquan; Jaiswal, Ajay; Wang, Song; Tewfik, Ahmed; Shih, George; Ding, Ying; Peng, Yifan is a scholarly article available to read on EtoBox.

What is Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays about?

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. In this paper, we develop an end-to-end framework, ChexRadiNet, that can utilize the radiomics features to improve the abnormality classification performance. Specifically, ChexRadiNet first applies a light-weight but efficient triplet-attention mechanism to classify the chest X-rays and highlight the abnormal regions. Then it uses the generated class activation map to extract radiomic features, which further guides our model to learn more robust image features. After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions. We evaluate the ChexRadiNet framework using three public datasets: NIH ChestX-ray, CheXpert, and MIMIC-CXR. We find that

Author

Han, Yan; Chen, Chongyan; Tang, Liyan; Lin, Mingquan; Jaiswal, Ajay; Wang, Song; Tewfik, Ahmed; Shih, George; Ding, Ying; Peng, Yifan

Published

2020

Language

EN