Computer Vision in Clinical Pathology | Pharma Industry

The Solution Unveiled: Our Comprehensive Flow Diagram

Technology Stack

Problem Statement

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Inefficiency: Manual processes can be time-consuming and labor-intensive, resulting in delays and increased costs.

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Human error: Manual processes are subject to human error, which can lead to mistakes and inaccuracies in the data, leading to misdiagnosis or wrong treatment.

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Limited access to high-quality data: AI-based automated solutions can also enable a pharma research company to access large and high-quality data sets that may be difficult to obtain manually.

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Limited data analysis capabilities: Manual processes may not be able to handle or analyze large amounts of data, making it difficult to identify patterns or trends.

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Difficulty in reproducing results: Manual processes may make it difficult to reproduce research results, which can negatively impact the credibility of the research.

Solution Overview

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Artificial intelligence-based models provide significant benefits for pharmaceutical companies, patients, and society through scalable and cost-effective solutions.

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We have partnered with a startup to develop an open-source platform that uses image processing techniques to identify histology images based on texture, spectral, and structural features such as the nucleus.

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This solution is integrated with multiple datatypes for increasing the efficiency of analysis and improves success for biomarker development.

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The intent of this project is to train Computer Vision-based model that can classify pathology image tiles as benign or malignant.

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

Business Impact

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Increased efficiency and cost savings: Processes data much faster and more accurately than manual processes, leading to increased efficiency and cost savings.

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Improved accuracy: Reduces the potential for human error and increase accuracy in the diagnosis of cancer.

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Scalability: Large amounts of data and can be easily handled and scaled, making it possible to process large numbers of pathology images.

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Data analysis capabilities: Analyzes data in greater depth and identify patterns that may not be visible to the human eye, providing insights that can lead to new treatments and therapies.

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Reproducibility: Produces consistent and reproducible results, which can enhance the credibility of the research.

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A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

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50+

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40+

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