no code implementations • 18 Feb 2020 • Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura, Naoto Hayashi, Osamu Abe
In general, adversarial perturbations superimposed on inputs are realistic threats for a deep neural network (DNN).
1 code implementation • 31 Dec 2020 • Yuta Nakamura, Shouhei Hanaoka, Yukihiro Nomura, Naoto Hayashi, Osamu Abe, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki
One problem is that previous studies have assessed the risk for different real-world privacy leakage scenarios and attack methods, which reduces the portability of the findings.
no code implementations • 9 Apr 2021 • Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura, Takahiro Nakao, Tomomi Takenaga, Naoto Hayashi, Osamu Abe
Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra low-dose protocols.
no code implementations • 23 Aug 2022 • Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura, Naoto Hayashi, Osamu Abe
From birth to death, we all experience surprisingly ubiquitous changes over time due to aging.
no code implementations • 20 Dec 2022 • Hisaichi Shibata, Shouhei Hanaoka, Yang Cao, Masatoshi Yoshikawa, Tomomi Takenaga, Yukihiro Nomura, Naoto Hayashi, Osamu Abe
To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images.
no code implementations • 15 Aug 2023 • Tomohiro Kikuchi, Shouhei Hanaoka, Takahiro Nakao, Tomomi Takenaga, Yukihiro Nomura, Harushi Mori, Takeharu Yoshikawa
The generation of synthetic medical records using Generative Adversarial Networks (GANs) is becoming crucial for addressing privacy concerns and facilitating data sharing in the medical domain.