10 Aug 2021
Cervical cancer is the second most important cancer for women worldwide. The method uses deep convolutional neural networks. The method avoids medically critical mistakes using regression constraint in the loss term. The method uses interpretability to perform weakly supervised localisation. The method is integrated in a workflow that matches pathologist workflow. The method proposes to aggregate tile-level predictions to output a slide-level diagnosis.
08 Mar 2021
Juvenile-onset recurrent respiratory papillomatosis (JoRRP) is a condition characterized by the repeated growth of benign exophytic papilloma in the respiratory tract. The course of the disease remains unpredictable: some children experience minor symptoms, while others require multiple interventions due to florid growth. Our study aimed to identify histologic severity risk factors in patients with JoRRP. Forty-eight children from two French pediatric centers were included retrospectively.
22 Feb 2021
Deep learning methods are widely used for medical applications to assist medical doctors in their daily routine. While performances reach expert’s level, interpretability (highlighting how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field.
02 Nov 2020
Background. During this collaboration between CILcare and KeenEye Technologies, a full pipeline has been designed to automatically classify and quantify the number of hair cells in 3D cochlea images.
01 Oct 2020
Abstract Background. In recent years, deep learning has been increasingly applied to a vast array of ophthalmological diseases. Inherited retinal diseases (IRD) are rare genetic conditions with a distinctive phenotype on fundus autofluorescence imaging (FAF).
01 Sep 2020
For the past decade, the medical and computer science communities have been working together to develop efficient computer-aided decision tools, using technological advances in both fields, to improve research and diagnosis.
01 Sep 2019
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.