Title: End-to-end learning of pharmacological assays from high-resolution microscopy images. Abstract: 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). We compared a
novel CNN architecture called “GapNet” against four competing CNN architectures
and found that it performs on par with the best methods and at the same time
has the lowest training time. Our results demonstrate that end-to-end learning on
high-resolution imaging data is not only possible but even outperforms cell-centric
and segmentation-dependent approaches. Hence, the costly cell segmentation and
feature extraction steps are not necessary, in fact they even hamper predictive performance.
Our work further suggests that many pharmacological assays could
be replaced by high-resolution microscopy imaging together with convolutional
neural networks.