Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology

2023/06/20 by Jakub Caputa, Maciej Wielgosz, Caputa, Jakub +25 · 1 citation
Computer Science · Engineering · #AI in cancer detection #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image Processing Techniques and Applications #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2306.11848

openalex publication_date 2023/06/20 · openalex created_date 2023/06/24 · openalex updated_date 2026/07/28

Abstract

The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional contribution was the development of a novel dataset aimed at improving imaging quality in the presence of inaccurate focus. Our experimental results demonstrate that the integration of SR techniques into the segmentation pipeline can lead to a significant improvement of up to 25% in the mean average precision (mAP) segmentation metric. These findings suggest that leveraging SR architectures holds great promise for advancing the state of the art in cytology image analysis.

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