Journal Article

Deep generative abnormal lesion emphasization validated by nine radiologists and 1000 chest X-rays with lung nodules

Matsuzaki, Hirotaka, Hanaoka, Shouhei, Hayashi, Naoto, Nomura, Yukihiro, Shibata, Hisaichi, Abe, Osamu, Fujimoto, Kotaro, Sakamoto, Naoya, Nakao, Takahiro, Cho, Shinichi, Sato, Issei, Miki, Soichiro, Yoshikawa, Takeharu, Koyama, Hiroaki, Yamamichi, Nobutake, Takenaga, Tomomi, Kanemaru, Noriko, Nishiyama, Tomoya

2024

Overview

A general-purpose method of emphasizing abnormal lesions in chest radiographs, named EGGPALE (Extrapolative, Generative and General-Purpose Abnormal Lesion Emphasizer), is presented. The proposed EGGPALE method is composed of a flow-based generative model and L-infinity-distance-based extrapolation in a latent space. The flow-based model is trained using only normal chest radiographs, and an invertible mapping function from the image space to the latent space is determined. In the latent space, a given unseen image is extrapolated so that the image point moves away from the normal chest X-ray hyperplane. Finally, the moved point is mapped back to the image space and the corresponding emphasized image is created. The proposed method was evaluated by an image interpretation experiment with nine radiologists and 1,000 chest radiographs, of which positive suspected lung cancer cases and negative cases were validated by computed tomography examinations. The sensitivity of EGGPALE-processed images showed +0.0559 average improvement compared with that of the original images, with -0.0192 deterioration of average specificity. The area under the receiver operating characteristic curve of the ensemble of nine radiologists showed a statistically significant improvement. From these results, the feasibility of EGGPALE for enhancing abnormal lesions was validated. Our code is available at https://github.com/utrad-ical/Eggpale .

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Biology and Life Sciences / Care and treatment / Chest / Computed tomography / Datasets / Deep Learning / Diagnosis / Extrapolation / Flow mapping / Humans / Hyperplanes / Lesions / Lung cancer / Lung diseases / Lung Neoplasms - diagnostic imaging / Lung Neoplasms - pathology / Lung nodules / Medical imaging / Medicine and Health Sciences / Methods / Nodules / Normal distribution / People and Places / Physical Sciences / Practice / Radiographic Image Interpretation, Computer-Assisted - methods / Radiographs / Radiography / Radiography, Thoracic - methods / Radiologists / Research and Analysis Methods / ROC Curve / Sensitivity analysis / Solitary Pulmonary Nodule - diagnostic imaging / Solitary Pulmonary Nodule - pathology / Statistical analysis / Tomography, X-Ray Computed - methods / Wavelet transforms / X-rays