  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.
