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

• URL: http://arxiv.org/abs/2011.12506v3
• Date: Fri, 9 Jul 2021 20:29:44 GMT
• Title: Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays
• Authors: Yan Han, Chongyan Chen, Liyan Tang, Mingquan Lin, Ajay Jaiswal, Song Wang, Ahmed Tewfik, George Shih, Ying Ding, Yifan Peng
• Abstract summary: We develop an end-to-end framework, ChexRadiNet, that can utilize the radiomics features to improve the abnormality classification performance. We evaluate the ChexRadiNet framework using three public datasets: NIH ChestX-ray, CheXpert, and MIMIC-CXR.
• Score: 14.679677447702653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 ChexRadiNet outperforms the state-of-the-art on both disease detection (0.843 in AUC) and localization (0.679 in T(IoU) = 0.1). We will make the code publicly available at https://github.com/bionlplab/lung_disease_detection_amia2021, with the hope that this method can facilitate the development of automatic systems with a higher-level understanding of the radiological world.

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