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

arXiv: https://arxiv.org/abs/2111.05794 · HTML: https://mlsanity.com/paper/2111.05794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [W-Net: A Deep Model for Fully Unsupervised Image Segmentation](https://mlsanity.com/paper/1711.08506/) (2017)
- [Deep Learning for Whole Slide Image Analysis: An Overview](https://mlsanity.com/paper/1910.11097/) (2019)
- [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020)
- [Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image](https://mlsanity.com/paper/2106.12265/) (2021)
- [Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images](https://mlsanity.com/paper/2008.05332/) (2020)
- [Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network](https://mlsanity.com/paper/2106.10641/) (2021)
- [Effects of annotation granularity in deep learning models for histopathological images](https://mlsanity.com/paper/2001.04663/) (2020)
- [OpenHI2 -- Open source histopathological image platform](https://mlsanity.com/paper/2001.05158/) (2020)

## Built on by (most cited first, 1 of 1)

- [Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation](https://mlsanity.com/paper/2211.14491/) (2022)

## Abstract (arXiv)

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.