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researcher

N. Hayashi

5 papers hereh-index 405.6k citations200 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 5 papers where every author was matched, so the position is known.

fields
  • eess.IV3
  • cs.CL1
  • cs.LG1
same name
  • N. Hayashi — 44 papers, h 15
  • N. Hayashi — 9 papers, h 16
  • N. Hayashi — 8 papers
  • N. Hayashi — 7 papers, h 20
  • N. Hayashi — 4 papers, h 16
  • N. Hayashi — 3 papers, h 18

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202022
most citedKART: Parameterization of Privacy Leakage Scenarios from Pre-trained Language Models

6 citations · 9 across the 5 of their papers we have counts for

collaborators
Showing eess.IVShow all

3 papers · 1 filter

eess.IV2022★ 1 cited

Aging prediction using deep generative model toward the development of preventive medicine

Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura +2

From birth to death, we all experience surprisingly ubiquitous changes over time due to aging. If we can predict aging in the digital domain, that is, the digital twin of the human…

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…

eess.IV2020★ 2 cited

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…

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