Multicenter automatic detection of invasive carcinoma on breast whole slide images.

Rémy Peyret¹, Nicolas Pozin¹, Stéphane Sockeel¹, Solène-Florence Kammerer-Jacquet², Julien Adam³, Claire Bocciarelli⁴, Yoan Ditchi⁵, Christophe Bontoux⁶, Thomas Depoilly⁷, Loris Guichard⁸, Elisabeth Lanteri⁹, Marie Sockeel^1,1, Sophie Prévot⁸

¹Primaa, Paris, France.

²Rennes University hospital, France.

³Paris Saint-Joseph hospital, Paris, France.

⁴Brest university hospital, France.

⁵Saint-Antoine hospital, APHP, Paris, France.

⁶Henri-Mondor hospital, APHP, Paris, France.

⁷René Dubos hospital, Pontoise, France.

⁸Bicêtre hospital, APHP, Paris Saclay University, France.

⁹Nice University hospital, France.

PubMed PMC DOI PDF

Abstract

Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms th…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.