  Manual annotation of pathology slides for cancer diagnosis is laborious and
repetitive. Therefore, much effort has been devoted to develop computer vision
solutions. Our approach, (FLASH), is based on a Deep Convolutional Neural
Network (DCNN) architecture. It reduces computational costs and is faster than
typical deep learning approaches by two orders of magnitude, making high
throughput processing a possibility. In computer vision approaches using deep
learning methods, the input image is subdivided into patches which are
separately passed through the neural network. Features extracted from these
patches are used by the classifier to annotate the corresponding region. Our
approach aggregates all the extracted features into a single matrix before
passing them to the classifier. Previously, the features are extracted from
overlapping patches. Aggregating the features eliminates the need for
processing overlapping patches, which reduces the computations required. DCCN
and FLASH demonstrate high sensitivity (~ 0.96), good precision (~0.78) and
high F1 scores (~0.84). The average time taken to process each sample for FLASH
and DCNN is 96.6 seconds and 9489.20 seconds, respectively. Our approach was
approximately 100 times faster than the original DCNN approach while
simultaneously preserving high accuracy and precision.
