Improving the annotation process in computational pathology: from manual to semi-automatic approaches in digital nephropathology

Name: Improving the annotation process in computational pathology: from manual to semi-automatic approaches in digital nephropathology
Creator: Giorgio Cazzaniga
Published: 2024-02-16T07:37:24.287Z
Keywords: Pathology, Digital Pathology, Anatomical Pathology

Cazzaniga, Giorgio; L'Imperio, Vincenzo

doi:10.17632/c36ywkzrm9.1

Improving the annotation process in computational pathology: from manual to semi-automatic approaches in digital nephropathology

Published: 16 February 2024| Version 1 | DOI: 10.17632/c36ywkzrm9.1

Contributors:

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Description

The development of reliable artificial intelligence (AI) algorithms in pathology depends on solid ground truth provided by meticulous annotation of whole slide images (WSI), a time-consuming and operator-dependent process. A benchmark of the available annotation tools is performed to standardize and streamline this process.

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Institutions

University of Milano-Bicocca

Departments

School of Medicine and Surgery

Funders

Ministero della Salute
Italy
Grant ID: PNRR-MR1-2022-12375735
Ministero della Salute
Italy
Grant ID: GR-2021-12374235
Ministero dell'Università e della Ricerca
Italy
Grant ID: Dipartimenti di Eccellenza 2023-2027 (l. 232/2016, art. 1, commi 314 - 337)

Additional Metadata for University of Milano - Bicocca

Date the data was collected	2023-12-01T00:00:00.000Z
SSD Classification	MED/08 - ANATOMIA PATOLOGICA

Improving the annotation process in computational pathology: from manual to semi-automatic approaches in digital nephropathology

Description

Files

Institutions

Departments

Categories

Funders

Additional Metadata for University of Milano - Bicocca

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