PIMIP: An Open Source Platform for Pathology Information Management and Integration

• URL: http://arxiv.org/abs/2111.05794v1
• Date: Tue, 9 Nov 2021 10:00:59 GMT
• Title: PIMIP: An Open Source Platform for Pathology Information Management and Integration
• Authors: Jialun Wu, Anyu Mao, Xinrui Bao, Haichuan Zhang, Zeyu Gao, Chunbao Wang, Tieliang Gong, and Chen Li
• Abstract summary: Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. There is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections. PIMIP has developed the image annotation functions based on the visualization of digital pathological sections.
• Score: 4.6242686927494026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. The digital pathology platform can make the pathological resources digital and networked, and realize the permanent storage of visual data and the synchronous browsing processing without the limitation of time and space. It has been widely used in various fields of pathology. However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information. Most platforms cannot integrate image viewing, annotation and analysis, and text information management. To solve the above problems, we propose a comprehensive and extensible platform PIMIP. Our PIMIP has developed the image annotation functions based on the visualization of digital pathological sections. Our annotation functions support multi-user collaborative annotation and multi-device annotation, and realize the automation of some annotation tasks. In the annotation task, we invited a professional pathologist for guidance. We introduce a machine learning module for image analysis. The data we collected included public data from local hospitals and clinical examples. Our platform is more clinical and suitable for clinical use. In addition to image data, we also structured the management and display of text information. So our platform is comprehensive. The platform framework is built in a modular way to support users to add machine learning modules independently, which makes our platform extensible.

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