Title | Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays. |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Han Y, Chen C, Tang L, Lin M, Jaiswal A, Wang S, Tewfik A, Shih G, Ding Y, Peng Y |
Journal | AMIA Annu Symp Proc |
Volume | 2021 |
Pagination | 546-555 |
Date Published | 2021 |
ISSN | 1942-597X |
Keywords | Deep Learning, Humans, Lung Diseases, Radiography, Thorax, X-Rays |
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 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. |
Alternate Journal | AMIA Annu Symp Proc |
PubMed ID | 35308939 |
PubMed Central ID | PMC8861661 |