Search Results for author: Yukihiro Nomura

Found 6 papers, 1 papers with code

Method for Generating Synthetic Data Combining Chest Radiography Images with Tabular Clinical Information Using Dual Generative Models

no code implementations15 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.

regression Synthetic Data Generation

Local Differential Privacy Image Generation Using Flow-based Deep Generative Models

no code implementations20 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.

Image Generation

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

no code implementations9 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.

Computed Tomography (CT) SSIM

KART: Parameterization of Privacy Leakage Scenarios from Pre-trained Language Models

1 code implementation31 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.

On the Matrix-Free Generation of Adversarial Perturbations for Black-Box Attacks

no code implementations18 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).

Semantic Segmentation

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