Quantacell Uses Image Analysis and Machine Learning for Biological and Medical Applications
“One of the main reasons that we won the hackathon was that, thanks to MATLAB, we could rapidly prototype and test our ideas. Within 24 hours, we had already developed an end-to-end application.”
Key Outcomes
- A flexible environment that is easy to use for rapid prototyping
- A unified tool for the entire workflow, from annotation and visualization to algorithm development
- Built-in machine learning and deep learning algorithms
Biological and medical applications generate an enormous number of high-resolution images, annotated extensively for many different purposes. The ability to interpret these images automatically enables Quantacell to answer new research questions more rapidly and ensure reproducibility.
Using MATLAB®, Quantacell has developed image analysis, machine learning, and deep learning prototypes for various applications. For example, they trained a deep learning model to help pathologists analyze kidney function from biopsy samples, achieving better results than visual inspection alone. They also used MATLAB to develop a complete algorithm combining deep learning and machine learning techniques in 24 hours, contributing to Quantacell winning a skin cancer–themed hackathon.
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