Book a Demo
Book a Demo
Meet2Win
Our Team
Meet2Win

By Shazia Osman (Senior BD) - 08 Apr 2021

Keen Eye is attending Meet2Win on May 6th and 7th! Connect with Keen Eye to discuss how we can help you propel computational pathology to the next level for better #drugdevelopment.
AACR Annual Meeting 2021
Our Team
AACR Annual Meeting 2021

By Paul Klein (Data Scientist), Thomas Le Meur (Data Scientist) - 08 Apr 2021

Keen Eye is presenting a scientific poster on April 10th-15th. Learn how we design deep learning workflows to quantify the H-Score on immunohistochemistry images!
Spatial Biology
Our Team
Spatial Biology

By Shazia Osman (Senior BD) - 02 Apr 2021

Keen Eye is attending the digital Spatial Biology Congress from April 14th to the 16th!
Biomarker Series Week
Our Team
Biomarker Series Week

By Sylvain Berlemont (CEO), Shazia Osman (Senior BD), Kevin Adam (Senior BD) - 02 Apr 2021

Keen Eye is attending the Biomarker Series Week starting May 17th to the 21st! Our CEO, Sylvain Berlemont will introduce Keen Eye in a Thought Leader presentation and our business team will be happy to meet you to discuss about your biomarker research and analysis projects.
Global Nash
Our Team
Global Nash

By Paul Bioche (Customer Success Manager) - 02 Apr 2021

Keen Eye is attending the global virtual NASH Congress from April 28th to 29th! Our team is looking forward to connecting with you and to discuss how Keen Eye helps its customers to accelerate their research and clinical trials.
Choosing the right resolution to train Deep Learning models on Histopathology images
PLATFORM
Choosing the right resolution to train Deep Learning models on Histopathology images

By David Guet (Digital Pathology Specialist), Melanie Lubrano (Data Scientist) - 30 Mar 2021

Histopathology is the analysis of tissue samples under a microscope in order to establish the severity of a disease. More precisely, it concerns the examination of tissue extracted from surgery, biopsy or autopsy. The tissue extracted from the body of the patient is placed in a fixative medium in order to prevent decay. It is then embedded in a cassette in wax before being sliced in thin microtome sections. Thin tissue sections are stained using different staining protocols (H&E, chromogenic, immunofluorescence, etc.) and mounted on glass slides before observation.
Histological Severity Risk Factors Identification in Juvenile-Onset Recurrent Respiratory Papillomatosis: How Immunohistochemistry and AI Algorithms Can Help?
Deep Learning
Histological Severity Risk Factors Identification in Juvenile-Onset Recurrent Respiratory Papillomatosis: How Immunohistochemistry and AI Algorithms Can Help?

08 Mar 2021

Juvenile-onset recurrent respiratory papillomatosis (JoRRP) is a condition characterized by the repeated growth of benign exophytic papilloma in the respiratory tract. The course of the disease remains unpredictable: some children experience minor symptoms, while others require multiple interventions due to florid growth. Our study aimed to identify histologic severity risk factors in patients with JoRRP. Forty-eight children from two French pediatric centers were included retrospectively.
Advancing Medical Image Analysis Through Data Science, Artificial Intelligence, and Deep Learning
Our Team
Advancing Medical Image Analysis Through Data Science, Artificial Intelligence, and Deep Learning

By Eunice Stennett, David Guet, Hippolyte Heuberger - 02 Mar 2021

Data science is the art of extracting and interpreting relevant information from large amounts of complex data. It has evolved rapidly over the years and is now recognized as a valuable medical image analysis tool. Until recently, the field of computational pathology relied on machine learning (ML) methods. These are highly-supervised algorithms designed to learn human-crafted annotations, and extract and decipher relevant features from their analysis. Keen Eye image-based biomarker analysis, on the other hand, relies on deep-learning (DL) methods. Those semi-supervised or unsupervised algorithms improve feature extraction and interpretability from translational to clinical research.
Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging
Deep Learning
Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging

22 Feb 2021

Deep learning methods are widely used for medical applications to assist medical doctors in their daily routine. While performances reach expert’s level, interpretability (highlighting how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field.
Support for Tissue Microarray (TMA)
PLATFORM
Support for Tissue Microarray (TMA)

By David Guet, Thomas Le Meur - 05 Feb 2021

For years now, digital pathology has seen the emergence of a plethora of heterogeneous different image types designed for clinical research use or routine diagnostics. Among them one can find tissue microarrays, or TMAs, introduced in the 1990s. TMA images differ from standard histopathological samples; instead of having a whole tissue section on a single slide, we find a large number of tissue cores organized on a grid, from different patients and, possibly from different tissue types. On one TMA slide, we can find several hundred samples.
Load More
10 of 27