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LifeArc: Advanced ML image processing of biological data sets
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LifeArc: Advanced ML image processing of biological data sets

Life Science Medical Reaserch Charity

To create a solution that would be technologically advanced enough to meet LifeArc’s innovation objectives.
Validation that a state-of-the-art neural network system is capable of processing difficult data sets.
At the beginning of their cooperation, Objectivity conducted a comprehensive project validation workshop with LifeArc’s team of key stakeholders, including a team of SMEs —scientists doing both the “wet work” and analysing the imaging data.

Initial Workshop
During the 3-day workshop, Objectivity was able to define the business case and the Proof of Concept (PoC), which would aim to address all of the scientists’ identified challenges. The teams worked together to identify the type of solution that would bring the company the most significant operational benefits. A Business Analyst, Developer and two Data Scientists took part in the workshop to ensure all of the most important project areas would be covered.

Proof of Concept
The Objectivity team built the backend of an analytics system, implementing it on the Azure Cloud with the use of Azure Machine Learning (ML). The developed backend solution — IRIS — is capable of processing and analysing entire data sets in an end-to-end manner. It covers such areas as cell detection using a convolutional neural network, single cell extraction, feature extraction using a convolutional autoencoder, dimensionality reduction, and clustering. IRIS can also detect analysed cells’ nuclei with the use of a specialised neural network. The application separates entire cells from the processed images and the autoencoder extracts relevant features from the single cell images.
Moreover, the solution’s dimensionality reduction feature allows for the visualisation of the groupings of single cells on a 2D plane. All this enables LifeArc’s scientists to investigate the results in terms of biological plausibility and to apply corrections, if necessary. The scientists don’t have to study machine learning in order to fine-tune the results and imbue them with their expert knowledge — all this is accomplished through a UI that facilitates the process. At the same time, the scientists have the control necessary to achieve the desired biologically plausible results.

Testing Phase
Once a preliminary version of the IRIS PoC was built, the application underwent an extensive testing phase. The system was tested iteratively, in line with the agile way of working, allowing LifeArc to evaluate its functionalities on an ongoing, regular basis. The Objectivity team adapted the application during each testing iteration to meet LifeArc’s feedback and requests.

Main technology

Azure Cloud



Supporting technologies

Azure Cloud
Azure Machine Learning


Proof of Concept



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