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
quantification, feature generation, feature space analysis, and explainability.
Whole-slide image processing is highly optimized, enabling whole-slide tile
extraction at 40X magnification in 2.5 seconds per slide. The
framework-agnostic data processing pipeline enables rapid experimentation with
new methods built with either Tensorflow or PyTorch, and the graphical user
interface supports real-time visualization of slides, predictions, heatmaps,
and feature space characteristics on a variety of hardware devices, including
ARM-based devices such as the Raspberry Pi.

Slideflow 是一种灵活的深度学习库，支持数字病理学的广泛深度学习方法，并包括快速的整个幻灯片接口来部署已训练好的模型，同时也具有高度优化的整个幻灯片图像处理等特点。