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.

本文提出了一个深度学习管道，以多个实例学习和注意力机制对组织检查图像进行分类，并成功实现了对鼻咽癌的 LMP1 状态预测。通过可视化注意力分数，该方法还允许对模型的解释性进行检查。