Sizing biological cells and saving lives using AI
Who is this presentation for?
- Data scientists and machine learning enthusiasts
Solutions that can generate accurate estimates of counts are in demand, whether for tallying the number of people in a video frame, counting the number of endangered animals, estimating the number of objects or shapes in a picture, or for a variety of similar industry applications. Srikanth Gopalakrishnan introduces novel crowd-counting techniques and their applications, including a pharma case study to show how it was used for drug discovery to bring about 98% savings in drug characterization efforts.
Traditional crowd-counting methods and models that use detection or regression-based approaches have been plagued by challenges such as occlusion, nonuniform distribution, perspective distortion, camera angles, and background clutter. They aren’t robust and often fail with even simple changes to the planned scenarios. Deep learning-based crowd-counting solutions offer an excellent recourse to such problems. Cascaded convolutional neural networks (CNNs) use density-based estimations to preserve the spatial information and can localize the count and estimate area of cells. Such neural network architectures capture the global and local features and have drastically improved over the past months to achieve remarkable accuracy. There are several experimental architectures, such as cascaded CNNs and multicolumn CNNs.
Pharma companies develop generic drugs by determining the patented drug’s composition. Solid-state characterization is a critical process in determining similarity of composition with an in-house drug formulation. This is usually done through shape classification on a microscopic liposome image. Cells are counted and areas estimated to measure the similarity.
This is a painful, manual process performed by pathologists. AI can help simplify this task. Gramener used a deep learning-based algorithm to automate this two-step process of counting cells and estimating the areas of cells. The task which took hundreds of hours for every set of 10 images was cut down to under 30 minutes. This led to huge savings in time, apart from helping improve accuracy. This solution was productionized by packaging it as a visual deep learning application. The interactive UI helped keep humans in the loop.
- A basic understanding of the data science workflow
What you'll learn
- Discover an application of AI to a practical use case
- Understand the impact created in healthcare and potential approval by FDI
Srikanth Gopalakrishnan is a senior data scientist at Gramener, Bangalore office. He works on applying deep learning and machine learning approaches and probabilistic modeling in diverse fields. He comes from a solid mechanics background with a master’s degree in simulation sciences from RWTH Aachen University, Germany. After a short stint at the Aeronautics Department, Purdue University, he returned to India and transitioned to data science.
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