Our Publications
We are defining a new approach.
Deep Learning is able to identify morphological abnormalities that are currently unknown to pathology classifications. By combining it with medical expertise, we propose new tumoral biomarkers that refine the diagnosis and predict the patient’s response to the therapeutic arsenal available to doctors.
Learn more:Breast-NEOprAIdict: Predicting response of breast cancer patients treated with neoadjuvant chemotherapyBreast-NEOprAIdict: Predicting response of breast cancer patients treated with neoadjuvant chemotherapy
Learn more:MultiVarNet: Predicting tumour mutational status at the protein levelMultiVarNet: Predicting tumour mutational status at the protein level
Learn more:Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational statusPreliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status
Learn more:Inter-semantic domain adversarial in histopathological imagesInter-semantic domain adversarial in histopathological images
Learn more:p16/Ki67 AutoReader: Retrospective diagnostic study of performancep16/Ki67 AutoReader: Retrospective diagnostic study of performance
Learn more:A deep learning solution for triaging patient with cancer according to their predicted mutational status using histopathological images