ML-Driven Blood Cell Identification

Project overview

A self-learning microscope image analysis tool designed to identify immunofluorescence labeled cells for more efficient diagnostics of tumors. Pre-trained neural network solution able to recognize different types of blood cells by analyzing imagery acquired from microscopes.

Key features and highlights

Providing the exact location of the target cells in clinical images
Drastically cutting the time needed for pathogenic cell identification
Improving the accuracy and efficiency of molecular diagnostics

Analyzing microscope images to identify immunofluorescence labeled tumor cells
Continuous improvement with as the machine learning-based component processes new input

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