HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images

HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images

<p>We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed...

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Bibliographic Details
Main Author: Zahoor Ahmad (358103) (author)
Other Authors: Khaled Al-Thelaya (17302711) (author), Mahmood Alzubaidi (15740693) (author), Faaiz Joad (21015119) (author), Nauman Ullah Gilal (17302714) (author), William Mifsud (1394) (author), Sabri Boughorbel (846228) (author), Giovanni Pintore (17302717) (author), Enrico Gobbetti (17302720) (author), Jens Schneider (16885948) (author), Marco Agus (8032898) (author)
Published: 2025
Subjects:
Engineering
Biomedical engineering
Health sciences
Health services and systems
Information and computing sciences
Artificial intelligence
Computer vision and multimedia computation
Histopathology
AI-based annotation
Nuclei localization
Kernel density estimation
Morse–Smale complex
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