Slideflow: deep learning for digital histopathology with real-time whole-slide visualization
James M. Dolezal, Sara Kochanny, Emma Dyer, Siddhi Ramesh, Andrew Srisuwananukorn, Matteo Sacco, Frederick M. Howard, Anran Li, Prajval Mohan, Alexander T. Pearson
TL;DR
Slideflow is a deep learning library for digital histopathology that offers a flexible and interactive interface for analyzing whole-slide images.
Contribution
Slideflow introduces a framework-agnostic deep learning library with real-time whole-slide visualization and efficient data processing tools.
Findings
Slideflow enables whole-slide tile extraction at 40x magnification in 2.5 seconds per slide.
The library supports rapid experimentation with deep learning methods using either Tensorflow or PyTorch.
It includes tools for stain normalization, weakly-supervised classification, and real-time visualization on various hardware.
Abstract
Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty…
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Taxonomy
TopicsLabor Law and Work Dynamics · Human Rights and Immigration · Employment, Labor, and Gender Studies