Case Studies

Case Studies Histological slides of mice with inflammatory bowel syndrome were analyzed by our AI model to score the severity based on cellular infiltration. Data scientists at Keen Eye used the multiple instance learning (MIL) method to divide up a large slide into small tiles for training the AI. The self-learning algorithm scores each tile [...]

HalioDx and KEEN EYE partner to empower IMMUNOSCORE® with Artificial Intelligence

HalioDx, an immuno-oncology diagnostic company pioneering the immunological diagnosis of cancers with Immunoscore® and Keen Eye, an innovative company specialized in image analysis technologies based on artificial intelligence announced today a strategic partnership to develop a cloud-based platform and applications. The partnership will create the very first cloud-based computational pathology system supported by the high […]

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Series A Fundraising

KEEN EYE Raises EUR 6 Millions to Empower Biomedical Labs with AI _____ This funding will enable the company to deploy internationally its latest machine learning technologies for laboratories and biomedical research. Paris, April 29, 2019 – KEEN EYE, a company that designs, develops and markets machine learning solutions for the research and biomedical sectors, […]

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  • Case Studies

    Histological slides of mice with inflammatory bowel syndrome were analyzed by our AI model to score the severity based on cellular infiltration. Data scientists at Keen Eye used the multiple instance learning (MIL) method to divide up a large slide into small tiles for training the AI. The self-learning algorithm scores each tile for its contribution to the final classification. A heatmap is then generated to reveal its decision-making process, making this an excellent tool for biomarker discovery projects. Read more here.

    We developed a deep learning training strategy to improve the classification of squamous cells for cervical cancer screening. Traditional deep learning models have huge drawbacks in the misclassification between normal and carcinoma-in-situ, the most severe form of the cancer. We evaluated a new architecture that takes into account the distances between classes and introduced penalties depending on the severity of the mistakes. The model showed a 94% accuracy on normal/abnormal classification. Furthermore, to explain the model’s prediction, we computed each pixels contribution to the prediction. Strong contribution from the nucleus region confirms that the model has learned the relevant features that match with medical consensus. Read more here.

    Developing computer algorithms that use classic feature analysis to classify among lung cancer subtypes of adenocarcinoma and squamous carcinoma, requires that pathologists annotate hundreds of cells on the training slides. Here we demonstrate the effectiveness of training the deep learning AI model on the whole slide image: using only one annotation – the slide-level diagnosis. Furthermore, we demonstrate that AI can accurately highlight the regions of interest (hotspots) where the pathologist can investigate further for insights. Read more here.

    Automating the analysis of IVCM (in vitro confocal microscopy) images can aid in the early detection of Sjögren syndrome, and prevent complications. The technical challenge, however, lies in the limited available data for training the deep learning model. Keen Eye researchers collaborated with clinicians at the Quinze-Vingts National Ophthalmology Hospital in Paris to design an innovative hybrid transfer learning model. By directing the AI model to focus on learning relevant features, we improved the model’s ability to converge using a small training sample from 80 patients. The resulting predictive accuracy of 81% shows great promise with this technique. Read more here.