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

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

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.