  Osteosarcoma is the most common primary malignant bone tumor, which has high
mortality due to easy lung metastasis. Osteosarcoma is a highly anaplastic,
pleomorphic tumor with a variety of tumor cell morphology, including fusiform,
oval, epithelial, lymphocyte like, small round, transparent cells, etc. Due to
the multiple patterns of osteosarcoma cell morphology, pathologists have
differences in the classification (viable tumor, necrotic tumor, non-tumor) of
osteosarcoma. Therefore, automatic and accurate recognition algorithms can help
pathologists greatly reduce time and improve diagnostic accuracy. In recent
years, deep learning technology has made great progress in the field of natural
images and medical images, and has achieved excellent results beyond human
performance in classification. In this paper, we propose a Deep Model with
Siamese Network (DS-Net) for automatic classification in Hematoxylin and Eosin
(H&E) stained osteosarcoma histology images.
