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- W2966854533 abstract "This article adresses the problem of automatic squamous cells classification for cervical cancer screening using Deep Learning methods. We study different architectures on a public dataset called Herlev dataset, which consists in classifying cells, obtained by cervical pap smear, regarding the severity of the abnormalities they represent. Furthermore, we use an attribution method to understand which cytomorphological features are actually learned as discriminative to classify severity of the abnormalities. Through this paper, we show how we trained a performant classifier: 74.5% accuracy on severity classification and 94% accuracy on normal/abnormal classification." @default.
- W2966854533 created "2019-08-13" @default.
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- W2966854533 date "2019-08-07" @default.
- W2966854533 modified "2023-09-27" @default.
- W2966854533 title "Regression Constraint for an Explainable Cervical Cancer Classifier." @default.
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