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Affinity Workshop: Women in Machine Learning

Self Supervised Learning in Microscopy

Aastha Jhunjhunwala · Siddha Ganju


We use SSL as a tool to explore a public microscopy dataset consisting of multi-channel (3+), non-composite microscopy images, where each channel represents a different stain. We present an extensible pipeline to perform self-supervised training using a contrastive learning approach. The workflow involves three parts: (1) Pre-Training phase which allows us to run several state of the art SSL algorithms such as SimCLR 2, MOCO, PIRL and compare algorithm performance on loss function, evaluate linear model for quality of embeddings and compute requirements (time to convergence and speed of convergence). (2) We then do full-tuning on the training dataset by deploying the model weights from the pre-training phase and evaluate the classification task for the labeled dataset. We learn some key insights and takeaways including amount of information in each image channel, channel wise performance, class or siRNA relations and well, plate and stain relations, (3) finally, we perform large scale visualization and with domain experts and analyze any latent biological information that was detected by the SSL models. We conclude that SSL cannot replace the existing supervised and unsupervised learning algorithms for our dataset, instead it can be used as a tool to expedite work flows and bolster the existing learning methodologies by providing a more meaningful starting point. However, SSL algorithms are heavily reliant on and constrained by the quantity and quality of data available. In spite of the limitations, they provide an efficient, reusable and cost effective means to derive meaning out of unlabeled data without the intervention of a domain expert. SSL experimentation can be a time consuming process, we would like to highlight the pros and cons of this learning paradigm based on our work and dataset, and to help future researchers make educated decisions on what dataset features and tasks make SSL more suitable.

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