Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours

Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours

Performing a mitosis count (MC) is the diagnostic task of histologically grading canine Soft Tissue Sarcoma (cSTS). However, mitosis count is subject to inter- and intra-observer variability. Deep learning models can offer a standardisation in the process of MC used to histologically grade canine So...

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Bibliographic Details
Main Authors: Taranpreet Rai, Ambra Morisi, Barbara Bacci, Nicholas James Bacon, Michael J. Dark, Tawfik Aboellail, Spencer A. Thomas, Roberto M. La Ragione, Kevin Wells
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Cancers
Subjects:
artificial intelligence
deep learning
canine Soft Tissue Sarcoma
canine Perivascular Wall Tumour
digital pathology
object detection
Online Access:https://www.mdpi.com/2072-6694/16/3/644

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