Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier.

Antoine Pirovano¹, Leandro G Almeida², Said Ladjal³, Isabelle Bloch⁴, Sylvain Berlemont²

¹Keen Eye, 74 rue du Faubourg Saint-Antoine, Paris 75012, France; LTCI, Telecom Paris, Institut Polytechnique de Paris, 19 Place Marguerite Perey, Palaiseau 91120, France. Electronic address: antoine.pirovano@keeneye.tech.

²Keen Eye, 74 rue du Faubourg Saint-Antoine, Paris 75012, France.

³LTCI, Telecom Paris, Institut Polytechnique de Paris, 19 Place Marguerite Perey, Palaiseau 91120, France.

⁴LTCI, Telecom Paris, Institut Polytechnique de Paris, 19 Place Marguerite Perey, Palaiseau 91120, France; Sorbonne Université, CNRS, LIP6, Paris, France.

PubMed DOI

Abstract

While pap test is the most common diagnosis methods for cervical cancer, their results are highly dependent on the ability of the cytotechnicians to detect abnormal cells on the smears using brightfield microscopy. In this paper, we propose an explainable region classifier in whole slide images that could be used by cyto-pathologists to handle efficiently these big images (100,000x100,000 pixels).…

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