  Estimating over-amplification of human epidermal growth factor receptor 2
(HER2) on invasive breast cancer (BC) is regarded as a significant predictive
and prognostic marker. We propose a novel deep reinforcement learning (DRL)
based model that treats immunohistochemical (IHC) scoring of HER2 as a
sequential learning task. For a given image tile sampled from multi-resolution
giga-pixel whole slide image (WSI), the model learns to sequentially identify
some of the diagnostically relevant regions of interest (ROIs) by following a
parameterized policy. The selected ROIs are processed by recurrent and residual
convolution networks to learn the discriminative features for different HER2
scores and predict the next location, without requiring to process all the
sub-image patches of a given tile for predicting the HER2 score, mimicking the
histopathologist who would not usually analyze every part of the slide at the
highest magnification. The proposed model incorporates a task-specific
regularization term and inhibition of return mechanism to prevent the model
from revisiting the previously attended locations. We evaluated our model on
two IHC datasets: a publicly available dataset from the HER2 scoring challenge
contest and another dataset consisting of WSIs of gastroenteropancreatic
neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the
proposed model outperforms other methods based on state-of-the-art deep
convolutional networks. To the best of our knowledge, this is the first study
using DRL for IHC scoring and could potentially lead to wider use of DRL in the
domain of computational pathology reducing the computational burden of the
analysis of large multigigapixel histology images.
