CE-marked and patent-protected automated biopsy and smear image readers. Our medical device software improves the reliability of screening and diagnosis by providing a fast, automated dual reading. They then show the most at-risk areas to the pathologist, who will decide on the diagnosis.
These tools make the cancer diagnosis more reliable by acting as quality controls to support physicians.
This programme has been awarded a French Tech Emergence grant and is supported by the Bourgogne-Franche-Comté region.

We are developing a virtual molecular biology solution that predicts the presence of molecular tumor alterations (DNA sequencing, transcriptomics, epigenetics, etc.) based on the analysis of morphological anomalies. The aim is to optimize access to molecular biology platforms to urgently orient patients who can respond to innovative targeted therapies and help reducing not relevant testing. We are working on all solid tumors, for more information we invite you to consult our publications.

This project is a 2022 winner of the Bpifrance i-Lab national innovation competition.

The routine use of neural networks is still a challenge. Indeed, the performance of the algorithms tends to drop once used in real life due to the variability of the data to be taken into account (type of
scanner, staining machine, technician, etc.). Moreover, neural networks are often very confident in their predictions, even when they are wrong. We propose UmmonQC, a set of tools to monitor the performance of AI algorithms, from their training phase to their daily use. Indeed, the trust that a doctor, a patient or a representative of a healthcare organization places in the results generated by an AI solution must be earned at every use.

This project has received a grant from the Fonds Régional d’Aide à l’Innovation of the Bourgogne-Franche-Comté region.