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Phenotype Image Data and Digital Learning Innovation (PhIDDLI)

Dr Michael Delves (LSHTM), Dr Rajat Dhawan (LSE), Dr Maurizio Gioli (SOAS) and Dr Edgar Whitley (LSE) - £32,000
1 October 2019 to 31 March 2021 (Sandpit Award BSA28)
Aim:

Feasibility study of commercial off-the-shelf (COTS) artificial intelligence and machine learning (AI/ML) solutions for automated cell isolation and identification of drug-treated malaria parasites from fluorescent microscope images to prepare them for future AI/ML-based phenotypic analysis.

Objectives:

  1. Using a pre-acquired dataset of raw microscope images of malaria parasites, evaluate and optimise AI/ML algorithms to efficiently and accurately extract cells of interest.
  2. Determine which AI/ML approach is most appropriate for this problem.
  3. Apply solution to a newly generated dataset.
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