  Convolutional neural networks (CNN) have achieved state of the art
performance on both classification and segmentation tasks. Applying CNNs to
microscopy images is challenging due to the lack of datasets labeled at the
single cell level. We extend the application of CNNs to microscopy image
classification and segmentation using multiple instance learning (MIL). We
present the adaptive Noisy-AND MIL pooling function, a new MIL operator that is
robust to outliers. Combining CNNs with MIL enables training CNNs using full
resolution microscopy images with global labels. We base our approach on the
similarity between the aggregation function used in MIL and pooling layers used
in CNNs. We show that training MIL CNNs end-to-end outperforms several previous
methods on both mammalian and yeast microscopy images without requiring any
segmentation steps.
