Search Results for author:
Found 3 papers, 1 papers with code
Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation
The analysis is a manual process which is subject to a human error, so this paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches.
What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review
As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers).
Towards a Complete Pipeline for Segmenting Nuclei in Feulgen-Stained Images
We achieved an overall IoU of 0. 78, showing the affordability of the approach of nuclei segmentation on Feulgen-stained images.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
