DeepSlide
A sliding window framework for classifying high-resolution whole-slide microscopy and histopathology images using deep neural networks.
What is DeepSlide?
DeepSlide is an open-source deep learning framework for classifying high-resolution whole-slide images (WSIs) in digital pathology and microscopy. It uses a sliding window technique to divide large images into manageable patches, trains convolutional neural networks to recognize histologic patterns, and aggregates predictions for slide-level diagnosis. The framework was developed to achieve pathologist-level accuracy in tasks like lung adenocarcinoma subtyping.
Researchers and computational pathologists working with whole-slide images, particularly those in medical imaging, histopathology, and digital pathology who need to automate slide classification.
DeepSlide provides a complete, reproducible pipeline tailored for histopathology images, including preprocessing, model training, and visualization. It is open-source, supports public datasets, and is designed to bridge deep learning with clinical pathology workflows.
Overview
Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.
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