Work

Re-identification from histopathology images

Year: 2024

Type: article

Abstract: scores of up to 80.1% and 77.19% on the LSCC and LUAD datasets, respectively, and with 77.09% on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient's privacy prior to publication.

Source: Medical Image Analysis

DOI: 10.1016/j.media.2024.103335

Authors: Jonathan Ganz, Jonas Ammeling, Samir Jabari, Katharina Breininger, Marc Aubreville

Institutions: Technische Hochschule Ingolstadt, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitätsklinikum Erlangen, Nuremberg Hospital, University of Würzburg, Flensburg University of Applied Sciences

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,970

Funders: Deutsche Forschungsgemeinschaft, Bayerisches Forschungsinstitut für Digitale Transformation

Awards: Robuste und akkurate multi Tumor, multi Species, multi Labor and multi Scanner Mitoseerkennung mittels großer Datensätze und Künstlicher Intelligenz, SFB 1540: Erforschung der Mechanik des Gehirns (EBM): Verständnis, Engineering und Nutzung mechanischer Eigenschaften und Signale in der Entwicklung, Physiologie und Pathologie des zentralen Nervensystems

OpenAlex ID: W4402621536

FWCI: 1.326

Cites: 50

Cited by: 9

Related to: 10