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:
, 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
Funding
Ministero della Salute
PNRR-MR1-2022-12375735
Ministero della Salute
GR-2021-12374235
Ministero dell'Università e della Ricerca
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 |