Computer Vision in Clinical Pathology | Pharma Industry

Business Challenges

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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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Inter-observer variability: Different researchers may have different levels of expertise and experience, which can lead to variations in their classification of image tiles, leading to inconsistent results.

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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.

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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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Potential legal liability: Incorrect diagnosis can lead to legal liability for the company.

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.

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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 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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Competitive advantage: By using AI-based solutions for pathology analysis, the company can gain a competitive edge over others that are still using manual processes.

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Better patient outcomes: Improves the accuracy of diagnosis, leading to better patient outcomes and reduced healthcare costs.

Technology Stack

Application Flow

Trusted and Proven Engagement Model

  • A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
  • Our NDA-driven process is established to keep clients’ data and IP safe and secure.
  • The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
  • This helps clarify the goals, and limitations, and deliver quality products & services.
  • Our engagement model defines the project size, project development plan, duration, concept, POC etc.
  • Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).
  • The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.
  • On signing the SOW, an official project kick-off meeting shall be initiated.
  • Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

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

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

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