Slideflow: A Unified Deep Learning Pipeline for Digital Histology

Slideflow provides a unified API for building and testing deep learning models for digital pathology, supporting both Tensorflow and PyTorch.

Slideflow includes tools for whole-slide image processing and tile extraction, customizable deep learning model training with dozens of supported architectures, explainability tools including heatmaps, mosaic maps, GANs, and saliency maps, analysis of activations from model layers, uncertainty quantification, and more. A variety of fast, optimized whole-slide image processing tools are included, including background filtering, blur/artifact detection, stain normalization, and efficient storage in *.tfrecords format. Model training is easy and highly configurable, with an easy drop-in API for training custom architectures. For external training loops, Slideflow can be used as an image processing backend, serving an optimized tf.data.Dataset or torch.utils.data.DataLoader to read and process slide images and perform real-time stain normalization.