Tong Yang, Ping Li, Bo Liu, Yuchun Lv, Dage Fan, Yuling Fan, Peizhong Liu, Yaping Ni
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引用次数: 0
Abstract
Endometrial cancer has the second highest incidence of malignant tumors in the female reproductive system, and accurate and efficient endometrial cancer pathology image analysis is one of the important research components of computer-aided diagnosis. However, endometrial cancer pathologic images have the challenges of smaller solid tumors, lesion areas varying in morphology, and difficulty distinguishing solid and non-solid tumors, which would impact the accuracy of subsequent pathological analyses. Therefore, an Endometrial Cancer Multi-class Transformer Network (ECMTrans-net) is proposed to improve the segmentation accuracy of endometrial cancer pathology images. On the one hand, an ECM-Attention is proposed, which can sequentially infer attention maps along three separate dimensions: channel, local spatial, and global spatial, and multiply the attention maps and the input feature map for adaptive feature refinement, solving the problems of the small size of solid tumors and similar characteristics of solid tumors to non-solid tumors and further improving the accuracy of segmentation of solid tumors. On the other hand, an ECM-Transformer is proposed, which can fuse multi-class feature information and dynamically adjust the receptive field, solving the issue of complex tumor features. Experiments on the solid tumor endometrial cancer pathological (ST-ECP) dataset show that the ECMTrans-net performs superior to state-of-the-art image segmentation methods, and the average values of Accuracy, MIoU, Precision, and Dice were 0.952, 0.927, 0.931 and 0.901, respectively.
期刊介绍:
The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.