Weakly-Supervised Cell Classification for Effective High Content Screening

Abstract

High Content Screening (HCS) allows for a complex cell analysis by combining fluorescent microscopy with the capability to automatically create a large number of images. Example of such cell analysis is examination of cell morphology under influence of a compound. Nevertheless, classical approaches bring the need for manual labeling of cell examples in order to train a machine learning model. Such methods are time- and resource-consuming. To accelerate the analysis of HCS data, we propose a new self-supervised model for cell classification: Self-Supervised Multiple Instance Learning (SSMIL). Our model merges Contrastive Learning with Multiple Instance Learning to analyze images with weak labels. We test SSMIL using our own dataset of microglia cells that present different morphology due to compound-induced inflammation. Representation provided by our model obtains results comparable to supervised methods proving feasibility of the method and opening the path for future experiments using both HCS and other types of medical images.