Sizing biological cells and saving lives using AI
AI techniques are finding applications in a wide range of applications.Crowd counting deep learning models have been used to count people, animals, and microscopic cells. This talk will introduce some novel crowd counting techniques and their applications. A pharma case study will be presented to show how it was used for drug discovery to bring about 98% savings in drug characterization efforts.
Solutions that can generate accurate estimates of counts are in demand, whether it is for tallying the number of people in a video frame, counting the number of animals of an endangered species, estimating the number of objects or shapes in a picture, or for a variety of similar industry applications.
*Shortcomings of traditional approaches: *
Traditional crowd counting methods and models that use detection or regression-based approaches have been plagued by challenges such as occlusion, non-uniform distribution, perspective distortion, camera angles, and background clutter. They are not robust and often fail with even simple changes to the planned scenarios.
*State-of-the-art CNNs in counting and area estimation: * Deep learning based crowd counting solutions offer an excellent recourse to such problems. Cascaded CNN’s 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 been drastically improved over the past months, to achieve remarkable accuracy. There are several architectures that are being experimented – such as cascaded CNNs, muti-column CNNs.
*Real-world case studies: * Pharma companies develop generic drugs by determining the patented drug’s composition. Solid-state characterization is a process that is critical in determining similarity of composition with 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. We 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. In this session, the case study will be presented with a live solution demo.
Srikanth is currently working as a Senior data scientist at Gramener, Bangalore office. He comes from a Solid mechanics background with a Masters in Simulation Sciences from RWTH Aachen University, Germany, and with work experience at EDF, Paris. After a short stint at Aeronautics department, Purdue University, he returned to India and transitioned to Data Science.
He works on interesting problems on various verticals. Srikanth is also a visiting faculty teaching data science at Department of Mechanical Engineering, PES University. He loves giving tech talks at various forums which helps him get interesting problems and suggestions from the