Title: Research on automatic annotation of pathological image detail information based on machine learning

Authors: Xiang Sun; Qianmu Li

Addresses: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China ' School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Abstract: Aiming at the inaccuracy of pathological image diagnosis results of traditional models, the machine learning technology is introduced and a new pathological image diagnosis method is then proposed. In this method, the accuracy of traditional pathological image diagnosis is transformed into the accuracy of detailed information annotation of pathological image. Pathological image pooling is completed through convolutional neural network, and some pathological images with dimensional blurring phenomenon are sharpened. On the basis of the pooling and sharpening of pathological image, the features of pathological image based on convolutional neural network are extracted. And then machine learning technology is used to annotate the extracted pathological image features and complete the automatic annotation of pathological image details. The experimental results show that the proposed model can improve the accuracy of pathological image feature annotation, and can accurately detect the pathological image of cancer cells, providing a new solution for pathological image diagnosis.

Keywords: pathological image; machine learning; information annotation; automatic detection.

DOI: 10.1504/IJICT.2020.110790

International Journal of Information and Communication Technology, 2020 Vol.17 No.4, pp.329 - 342

Received: 20 Jul 2019
Accepted: 28 Aug 2019
Published online: 29 Oct 2020 *

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