Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

Author:

He Keke1,Zhu Jun23,Li Limiao1,Gou Fangfang4ORCID,Wu Jia2345ORCID

Affiliation:

1. School of Computer Science and Engineering Changsha University Changsha China

2. Hunan University of Medicine General Hospital Huaihua China

3. Collaborative Innovation Center for Medical Artificial Intelligence and Big Data Decision Making Assistance Hunan University of Medicine Huaihua China

4. State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

5. Research Center for Artificial Intelligence Monash University Melbourne Clayton Australia

Abstract

AbstractArtificial intelligence decision systems play an important supporting role in the field of medical information. Medical image analysis is an important part of decision systems and an even more important part of medical diagnosis and treatment. The wealth of cellular information in histopathological images makes them a reliable means of diagnosing tumors. However, due to the large size, high resolution, and complex background structure of pathology images, deep learning methods still have various difficulties in the recognition of pathology images. Based on this, this study proposes a two‐stage continuous improvement‐based approach for pathology image recognition in medical decision systems. For pathology images with complex backgrounds, normalization and enhancement is performed to remove the effects of noise color, and light‐dark inconsistencies on the segmentation network. The continuous refinement PSP Net (CRPSPNet) is then designed for accurate recognition of the pathology images. CRPSPNet is divided into two stages: Pyramid Scene Parsing Network segmentation to obtain coarse segmentation results; and continuous refinement model refines the results of the first stage. Experiments using more than 1,000 osteosarcoma pathology images have shown that the method gives more accurate results with fewer computer resources and processing time than traditional optimization models. Its Intersection over Union achieves 0.76.

Funder

Natural Science Foundation of Hunan Province

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

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