Slideflow：用于数字组织病理学且支持实时全切片可视化的深度学习工具
Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization
AI总结：
针对组织病理学深度学习模型缺乏开源交互界面与跨框架实验困难的问题，开发了支持全切片图像处理、弱监督分类与实时可视化的灵活开源深度学习库Slideflow。
摘要：
深度学习方法已成为分析组织病理学图像的强大工具，但现有方法通常专用于特定领域和软件环境，且鲜有开源选项支持在交互式界面中部署模型。尝试不同的深度学习方法通常需要切换软件库并重新处理数据，降低了实验新架构的可行性与实用性。我们开发了一款名为Slideflow的灵活组织病理学深度学习库，该软件包支持广泛的数字病理学深度学习方法，并包含一个用于部署已训练模型的快速全切片界面。Slideflow包含用于全切片图像数据处理、高效染色归一化与增强、弱监督全切片分类、不确定性量化、特征生成、特征空间分析和可解释性的独特工具。全切片图像处理经过高度优化，能够在每张切片2.5秒的速度下实现40X放大倍数下的全切片切片提取。框架无关的数据处理流水线支持对基于Tensorflow或PyTorch构建的新方法进行快速实验，图形用户界面支持在多种硬件设备（包括如Raspberry Pi等基于ARM的设备）上实时可视化切片、预测、热图和特征空间特性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
