# FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

arXiv: https://arxiv.org/abs/2011.06033 · HTML: https://mlsanity.com/paper/2011.06033/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology](https://mlsanity.com/paper/2312.10944/) (2023)
- [OncoPetNet: A Deep Learning based AI system for mitotic figure counting on H&E stained whole slide digital images in a large veterinary diagnostic lab setting](https://mlsanity.com/paper/2108.07856/) (2021)
- [Code-free development and deployment of deep segmentation models for digital pathology](https://mlsanity.com/paper/2111.08430/) (2021)
- [Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images](https://mlsanity.com/paper/2112.03455/) (2021)
- [Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides](https://mlsanity.com/paper/2311.13261/) (2023)
- [Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion](https://mlsanity.com/paper/2406.14287/) (2024)
- [Deep learning-based compression of giga-resolution whole slide images](https://mlsanity.com/paper/2605.17668/) (2026)

## Abstract (arXiv)

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++ based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.