End-to-End Learning of Pharmacological Assays from High-resolution Microscopy Images
Sprache des Titels:
Neural Information Processing Systems (NIPS 2018)
Predicting the outcome of pharmacological assays based on high-resolution microscopy images of treated cells is a crucial task in drug discovery which tremendously increases discovery rates. However, end-to-end learning on these images with convolutional neural networks (CNNs) has not been ventured for this task because it has been considered infeasible and overly complex. On the largest available public dataset, we compare several state-of-the-art CNNs trained in an end-to-end fashion with models based on a cell-centric approach involving segmentation. We found that CNNs operating on full images containing hundreds of cells perform significantly better at assay prediction than networks operating on a single-cell level. Surprisingly, we could predict 29% of the 209 pharmacological assays at high predictive performance (AUC > 0.9).