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Abstract #4339

Transformer based Self-supervised learning for content-based image retrieval

Deepa Anand1, Chitresh Bhushan2, and Dattesh Dayanand Shanbhag1
1GE Healthcare, Bangalore, India, 2GE Global Research, Niskayuna, NY, United States


Diversity in training data encompassing variety of patient conditions is a recipe for the success of medical models based on DL. Ensuring diverse patient conditions is often impeded by the necessity to manually identify and include such cases, which is time-consuming and expensive. Here we propose a method of retrieving images similar to a handful of example images based on features learnt using self-supervised learning. We demonstrate the features learnt using SSL on transformer based networks are excellent feature learners which not only eliminates the need for annotation but enable accurate KNN based image retrieval matching the desired patient conditions.

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