Computer Vision in Clinical Pathology | OpenCV | Python |

Overview

The main  aim of the solution is to automate and accelerate  the discovery of new drugs and treatment possibilities. With help of  artificial intelligence based model, the  pharmaceutical companies, patients, and society are benefited at large with scalable and cost-effective solutions.  This open platform integrates multiple datatypes which helps to increase efficiency of analysis and improves success for bio marker development . It also deals with histology images where the texture, spectral and structural features such of the nucleus are identified with the help of Image processing.   The intent of this project is train Computer Vision-based Deep Learning models that can classify pathology image tiles as benign or malignant.

Application Flow

Solution Overview

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It deals with histology images where the texture, spectral and structural features such as the nucleus are identified with the help of Image processing.

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The structural features such as shape index, compactness, elliptic fit, distance, etc. of the nucleus.

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The spectral features involve finding out the optical density format of the given images and obtaining the stain vectors and intensity for the stains involved in the histology images such as Hematoxylin, Eosin, and Residual.

Business Value

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Aiding and automating the analysis of Pathology images in detecting cancers and other ailments provide a great value to the health care domain.

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Automating the analysis of terabytes of data to eliminate the chances of human error in early and accurate diagnosis of life-threatening ailments.

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Vision Intelligence models increase the efficiency of analysis and improve success for bio marker development that can be used in new drug discovery and treatment.

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