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Hauptverfasser: Arshad, M. Muneeb, Sajid, Hasan, Khan, M. Jawad
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2409.06742
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author Arshad, M. Muneeb
Sajid, Hasan
Khan, M. Jawad
author_facet Arshad, M. Muneeb
Sajid, Hasan
Khan, M. Jawad
contents Deep learning is popularly used for analyzing pathology images, but variations in image properties can limit the effectiveness of the models. The study aims to develop a method that transfers the variability present in the training set to unseen images, improving the model's ability to make accurate inferences. YOLOv5 was trained on peripheral blood and bone marrow sample images and Neural Color Transfer techniques were used to incorporate invariance. The results showed significant improvement in detecting WBCs from untrained samples after normalization, highlighting the potential of deep learning-based normalization techniques for inference robustness.
format Preprint
id arxiv_https___arxiv_org_abs_2409_06742
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Stain Normalization of Hematology Slides using Neural Color Transfer
Arshad, M. Muneeb
Sajid, Hasan
Khan, M. Jawad
Image and Video Processing
Deep learning is popularly used for analyzing pathology images, but variations in image properties can limit the effectiveness of the models. The study aims to develop a method that transfers the variability present in the training set to unseen images, improving the model's ability to make accurate inferences. YOLOv5 was trained on peripheral blood and bone marrow sample images and Neural Color Transfer techniques were used to incorporate invariance. The results showed significant improvement in detecting WBCs from untrained samples after normalization, highlighting the potential of deep learning-based normalization techniques for inference robustness.
title Stain Normalization of Hematology Slides using Neural Color Transfer
topic Image and Video Processing
url https://arxiv.org/abs/2409.06742