Revolutionize pathology analysis with Artificial Intelligence
The solution unveiled: our comprehensive flow diagram
Problem statement
Inefficiency: Manual processes can be time-consuming and labor-intensive, resulting in delays and increased costs.
Human error: If Manual processes are subject to human error, which can lead to mistakes and inaccuracies in the data, leading to misdiagnosis or wrong treatment.
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.
Limited data analysis capabilities: If Manual processes may not be able to handle or analyze large amounts of data, making it difficult to identify patterns or trends.
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
Artificial intelligence-based models provide significant benefits for pharmaceutical companies, patients, and society through scalable and cost-effective solutions.
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.
This solution is integrated with multiple datatypes for increasing the efficiency of analysis and improves success for biomarker development.
The intent of this project is to train Computer Vision-based model that can classify pathology image tiles as benign or malignant.
The structural features such as shape index, compactness, elliptic fit, distance, etc. of the nucleus.
Business impact
Increased efficiency and cost savings: Processes data much faster and more accurately than manual processes, leading to increased efficiency and cost savings.
Improved accuracy: Reduces the potential for human error and increase accuracy in the diagnosis of cancer.
Scalability: Large amounts of data and can be easily handled and scaled, making it possible to process large numbers of pathology images.
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.
Reproducibility: Produces consistent and reproducible results, which can enhance the credibility of the research.
