cuCIM - A GPU image I/O and processing library

Lee, Gregory; Bae, Gigon; Zaitlen, Benjamin; Kirkham, John; Choudhury, Rahul Nath

doi:10.25080/majora-1b6fd038-022

Cited by 2 publications

(2 citation statements)

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“…Slideflow supports nine slide scanner vendors (Table 1 ) and includes two slide reading backends – cuCIM [ 23 ], an efficient, GPU-accelerated slide reading framework for TIFF and SVS slides, and VIPS [ 24 ], an OpenSlide-based framework which adds support for additional slide formats. The first step in processing whole-slide images (WSI) for downstream deep learning applications is slide-level masking and filtering, a process that determines which areas of the slide are relevant and which areas should be ignored.…”

Section: Methodsmentioning

confidence: 99%

Slideflow: deep learning for digital histopathology with real-time whole-slide visualization

Dolezal,

Kochanny,

Dyer

et al. 2024

BMC Bioinformatics

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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 s 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.

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Section: Methodsmentioning

confidence: 99%

Slideflow: deep learning for digital histopathology with real-time whole-slide visualization

Dolezal,

Kochanny,

Dyer

et al. 2024

BMC Bioinformatics

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“…MONAI provides adapter tools to accommodate the use of third-party transforms from packages such as ITK [13], torchIO [17], Kornia [18], BatchGenerator [19], Rising [20], and cuCIM [21]. For example, cuCIM has implementations of optimized versions of several common transforms that are often used in digital pathology pipelines.…”

Section: Compatibility With Community-led Librariesmentioning

confidence: 99%

MONAI: An open-source framework for deep learning in healthcare

Cardoso¹,

Li²,

Brown³

et al. 2022

Preprint

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Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, welldocumented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

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cuCIM - A GPU image I/O and processing library

Cited by 2 publications

References 0 publications

Slideflow: deep learning for digital histopathology with real-time whole-slide visualization

Slideflow: deep learning for digital histopathology with real-time whole-slide visualization

MONAI: An open-source framework for deep learning in healthcare

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