  Histology images with multi-gigapixel of resolution yield rich information
for cancer diagnosis and prognosis. Most of the time, only slide-level label is
available because pixel-wise annotation is labour intensive task. In this
paper, we propose a deep learning pipeline for classification in histology
images. Using multiple instance learning, we attempt to predict the latent
membrane protein 1 (LMP1) status of nasopharyngeal carcinoma (NPC) based on
haematoxylin and eosin-stain (H&E) histology images. We utilised attention
mechanism with residual connection for our aggregation layers. In our 3-fold
cross-validation experiment, we achieved average accuracy, AUC and F1-score
0.936, 0.995 and 0.862, respectively. This method also allows us to examine the
model interpretability by visualising attention scores. To the best of our
knowledge, this is the first attempt to predict LMP1 status on NPC using deep
learning.
