3 papers
eess.IV2021
X2CT-FLOW: Maximum a posteriori reconstruction using a progressive flow-based deep generative model for ultra sparse-view computed tomography in ultra low-dose protocols
Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura +4
Ultra sparse-view computed tomography (CT) algorithms can reduce radiation exposure of patients, but those algorithms lack an explicit cycle consistency loss minimization and an ex…
cs.LG2020
On the Matrix-Free Generation of Adversarial Perturbations for Black-Box Attacks
Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura +2
In general, adversarial perturbations superimposed on inputs are realistic threats for a deep neural network (DNN). In this paper, we propose a practical generation method of such…
eess.IV2020
A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting
H. Shibata, S. Hanaoka, Y. Nomura +5
Oversight in medical images is a crucial problem, and timely reporting of medical images is desired. Therefore, an all-purpose anomaly detection method that can detect virtually al…