Your tissues have so much to tell
Next Generation Tumor Board Meetings.
Our mission is to optimize the management of cancer patients. Thanks to AI and digital pathology, we offer support for Tumor Board meetings by integrating diagnosis, molecular biology and therapeutic recommendations directly on the image of the digitized biopsy.
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.
We make Precision Oncology accessible.
Tissue images are a data gold mine. Pathologists analyze tissue architecture, cells and nuclei abnormalities, their special relationships at small and large scales. The computing power of the neural networks we design allows us to decode visually imperceptible information.
Our algorithms are designed to support the oncologist, surgeon, radiotherapist and pathologist during their Tumor Board meetings. They include a diagnostic aid for the pathologist and a virtual molecular biology analysis on the image (NGS, transcriptomics, epigenetics, etc.) to help the oncologist choose the right complementary examinations to carry out. Based on this information, our databases and the literature, we statistically estimate which treatment options have the best chance of working out for a specific patient.
Our technologies
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If you are interested in learning more about our products or are looking for expertise like ours for your own solutions, we would be happy to talk to you.
Key figures
Treat faster
3 min.
to analyze p16/ki67 marking with 100% sensitivity and 100% specificity with our AutoReader
Compared to 1h for the reference analysis, with 45.9% false negatives.
En savoir plus En savoir plusReduce costs
100%
of analyzed patients receive a molecular biology report at no additional cost.
Currently, 50% of patients miss out on effective targeted therapy because the right molecular biology tests have not been performed.
En savoir plus En savoir plusDiagnose precisely
20,56x
more likely to predict chemosensitivity with Chemo-prAIdict
Compared to the standard classification (AJCC, SBR) for neoadjuvant early breast cancer.
En savoir plus En savoir plusOur products
Read more:p16/Ki67 AutoReaderp16/Ki67 AutoReader
Revolutionizing Cervical Cancer Screening with Automated Cytology Join the Future of Cervical Cancer Screening with p16/Ki67 AutoReader of Ummon.
Read more:Ummon CrawlerUmmon Crawler
Precision Medicine in Oncology
50% of patients are missing the best therapy due to insufficient molecular testing. We have agame changer !
Read more:Chemo-prAIdictChemo-prAIdict
Next generation tumor boards
Predicting tumor chemosensitivity & relapse risk using Deep Learning.
Our partners
Our publications
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