Abstract：
:The most frequent extracranial solid tumors of childhood, named peripheral neuroblastic tumors (pNTs), are very challenging to diagnose due to their diversified categories and varying forms. Auxiliary diagnosis methods of such pediatric malignant cancers are highly needed to provide pathologists assistance and reduce the risk of misdiagnosis before treatments. In this paper, inspired by the particularity of microscopic pathology images, we integrate neural networks with the texture energy measure (TEM) and propose a novel network architecture named DetexNet (deep texture network). This method enforces the low-level representation pattern clearer via embedding the expert knowledge as prior, so that the network can seize the key information of a relatively small pathological dataset more smoothly. By applying and finetuning TEM filters in the bottom layer of a network, we greatly improve the performance of the baseline. We further pre-train the model on unlabeled data with an auto-encoder architecture and implement a color space conversion on input images. Two kinds of experiments under different assumptions in the condition of limited training data are performed, and in both of them, the proposed method achieves the best performance compared with other state-of-the-art models and doctor diagnosis.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Liu Y,Yin M,Sun Sdoi
10.1109/TMI.2020.3027547subject
Has Abstractpub_date
2021-01-01 00:00:00pages
395-404issue
1eissn
0278-0062issn
1558-254Xjournal_volume
40pub_type
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journal_title：IEEE transactions on medical imaging
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