  Predicting the mutation status of genes in tumors is of great clinical
significance. Recent studies have suggested that certain mutations may be
noninvasively predicted by studying image features of the tumors from Computed
Tomography (CT) data. Currently, this kind of image feature identification
method mainly relies on manual processing to extract generalized image features
alone or machine processing without considering the morphological differences
of the tumor itself, which makes it difficult to achieve further breakthroughs.
In this paper, we propose a pyramid focusing network (PFNet) for mutation
prediction and classification based on CT images. Firstly, we use Space Pyramid
Pooling to collect semantic cues in feature maps from multiple scales according
to the observation that the shape and size of the tumors are varied.Secondly,
we improve the loss function based on the consideration that the features
required for proper mutation detection are often not obvious in cross-sections
of tumor edges, which raises more attention to these hard examples in the
network. Finally, we devise a training scheme based on data augmentation to
enhance the generalization ability of networks. Extensively verified on
clinical gastric CT datasets of 20 testing volumes with 63648 CT images, our
method achieves the accuracy of 94.90% in predicting the HER-2 genes mutation
status of at the CT image.
