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:
, Vincenzo L'Imperio

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

Categories

Pathology, Digital Pathology, Anatomical Pathology

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 collected2023-12-01T00:00:00.000Z
SSD ClassificationMED/08 - ANATOMIA PATOLOGICA

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