Weakly Supervised Learning of Single-Cell Feature Embeddings

Juan C. Caicedo, Claire McQuin, Allen Goodman, Shantanu Singh, Anne E. Carpenter; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 9309-9318


Many new applications in drug discovery and functional genomics require capturing the morphology of individual imaged cells as comprehensively as possible rather than measuring one particular feature. In these so-called profiling experiments, the goal is to compare populations of cells treated with different chemicals or genetic perturbations in order to identify biomedically important similarities. Deep convolutional neural networks (CNNs) often make excellent feature extractors but require ground truth for training; this is rarely available in biomedical profiling experiments. We therefore propose to train CNNs based on a weakly supervised approach, where the network aims to classify each treatment against all others. Using this network as a feature extractor performed comparably to a network trained on non-biological, natural images on a chemical screen benchmark task, and improved results significantly on a more challenging genetic benchmark presented for the first time.

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author = {Caicedo, Juan C. and McQuin, Claire and Goodman, Allen and Singh, Shantanu and Carpenter, Anne E.},
title = {Weakly Supervised Learning of Single-Cell Feature Embeddings},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}