RSIP Vision is introducing Deep Learning in the biopsy analysis procedure. Here is how the benefits of tissue analysis with AI have become available. Biopsy is the key examination used to determine the presence of most malignant tumors. Notwithstanding its importance and the need for millions of examinations every year, the analysis of biopsy in the search of cancer cells has been based for many years on tedious work of expert pathologists. This has exacerbated the demand on this resource, which is always expensive and often scarcely available.

Pharma - Tissue Analysis

By introducing the revolutionary AI Deep Learning technologies, it has become possible to carry the important and critical tissue analysis with AI in a much more reliable and less human-intensive way.

There are many challenges to applying Deep Learning to biopsy procedures, starting from the detection of cells and the ability to segment clumps of packed cells into individual cells. Following these steps, more issues need to be solved in the classification of the cells.

Classification is highly dependent on the staining procedure, which is very variable from one lab to another and from one probe to the other. This high inter and intra variability makes it quite difficult to obtain objective and univocal results from biopsy analysis.

Automated Tissue Analysis with AI

RSIP Vision provides a solution to all these challenges, developing Artificial Intelligence algorithms for histopathology needs.

The immense breakthroughs given to us by deep learning technology enable us to achieve accuracy levels that were not previously possible, using only the classical methods of traditional computer vision.

The training stage is quite flexible and it can adjust to images from different sources to compensate for the specific tasks at hand.

With our automated application, the future of pharma and the medical imaging fields looks very promising. Diagnosis is becoming more precise, more robust and much faster. In addition, the ability to match specific treatments to each individual patient is becoming more and more realistic.

Do you want to benefit of the Deep Learning revolution? Ask our engineers to discuss with you the implementation of AI within your organization.

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