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Using Super-Resolution for Enhancing Visual Perception and Segmentation Performance in Veterinary Cytology.

Jakub CaputaMaciej WielgoszDaria ŁukasikPaweł RussekJakub GrzeszczykMichał KarwatowskiSzymon MazurekRafał FrączekAnna ŚmiechErnest JamroSebastian KoryciakAgnieszka Dąbrowska-BoruchMarcin PietrońKazimierz Wiatr
Published in: Life (Basel, Switzerland) (2024)
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) metric. These findings suggest that leveraging SR architectures holds great promise for advancing the state-of-the-art in cytology image analysis.
Keyphrases
  • convolutional neural network
  • deep learning
  • fine needle aspiration
  • high grade
  • ultrasound guided
  • artificial intelligence
  • high resolution
  • quality improvement
  • big data
  • optical coherence tomography
  • high density