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.
authors: Markus Hofmarcher Elisabeth Rumetshofer Sepp Hochreiter Günter Klambauer
hIndex: Sepp Hochreiter 43