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

Cardoso, M. Jorge; Li, Wenqi; Brown, Richard; Ma, Nic; Kerfoot, Eric; Wang, Yiheng; Murrey, Benjamin; Zhao, Chenglin; Yang, Dong; Nath, Vishwesh; He, Yufan; Xu, Zhenming; Hatamizadeh, Ali; Myronenko, Andriy; Zhu, Wentao; Liu, Yun; Zhang, Mingxin; Tang, Yucheng; Yang, Isaac; Zephyr, Michael; Hashemian, Behrooz; Alle, Sachidanand; Darestani, Mohammad Zalbagi; Budd, Charlie; Modat, Marc; Vercauteren, Tom; Wang, Guotai; Li, Yiwen; Hu, Yipeng; Fu, Yunguan; Gorman, Benjamin M.; Johnson, Hans J.; Genereaux, Brad; Erdal, Barbaros S.; Gupta, Vikas; Diaz-Pinto, Andrés; Dourson, Andre; Jaeger, Paul F.; Baumgärtner, Michael; Kalpathy-Cramer, Jayashree; Flores, Mona G.; Kirby, Justin; Cooper, Lee; Roth, Holger R.; Xu, Daguang; Bericat, David; Floca, Ralf; Zhou, S. Kevin; Shuaib, Haris; Farahani, Keyvan; Maier‐Hein, Klaus H.; Aylward, Stephen; Dogra, Prerna; Ourselin, Sébastien; Feng, Andrew

doi:10.48550/arxiv.2211.02701

Cited by 109 publications

(96 citation statements)

References 21 publications

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“…There are existing PyTorch-based ML libraries that similarly to fuse cater to researchers in the biomedical domain. Two examples of such prominent libraries are MONAI (Cardoso et al, 2022) and PyHealth (Zhao et al, 2021). MONAI is primarily focused on medical imaging applications.…”

Section: State Of the Fieldmentioning

confidence: 99%

FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

Golts¹,

Raboh²,

Shoshan³

et al. 2023

JOSS

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Machine Learning is at the forefront of scientific progress in Healthcare and Medicine. To accelerate scientific discovery, it is important to have tools that allow progress iterations to be collaborative, reproducible, reusable and easily built upon without "reinventing the wheel" for each task. FuseMedML, or fuse, is a Python framework designed for accelerated Machine Learning (ML) based discovery in the medical domain. It is highly flexible and designed for easy collaboration, encouraging code reuse. Flexibility is enabled by a generic data object design where data is kept in a nested (hierarchical) Python dictionary (NDict), allowing to efficiently process and fuse information from multiple modalities. Functional components allow to specify input and output keys, to be read from and written to the nested dictionary. Easy code reuse is enabled through key components implemented as standalone packages under the main fuse repo using the same design principles. These include fuse.data -a flexible data processing pipeline, fuse.dl -reusable Deep Learning (DL) model architecture components and loss functions, and fuse.eval -a library for evaluating ML models.

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Section: State Of the Fieldmentioning

confidence: 99%

FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

Golts¹,

Raboh²,

Shoshan³

et al. 2023

JOSS

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“…• Medical Open Network for AI (MONAI) [38]: A PyTorch-based framework that offers researchers pre-processing methods for medical imaging data, domain-specific implementations of machine learning architectures, and ready-to-use workflows for healthcare imaging. The actively maintained framework also provides APIs for integration into existing workflows.…”

Section: F3 Prominent Non-causal Toolsmentioning

confidence: 99%

A Review of the Role of Causality in Developing Trustworthy AI Systems

Ganguly¹,

Fazlija²,

Badar³

et al. 2023

Preprint

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State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.

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“…The TorchIO library 64 addresses the gap between CV and medical image data processing requirements providing functions for efficient loading, augmentation, preprocessing, and patch-based sampling of medical imagery. The medical open network for AI (MONAI) 66 is a PyTorch-based 67 framework that facilitates the development of diagnostic AI models with tutorials for classification, segmentation, and AI model deployment. Further efforts in this realm include NiftyNet, 68 the deep learning tool kit (DLTK), 69 MedicalZooPytorch, 70 and nnDetection.…”

Section: Image Synthesis Tools and Librariesmentioning

confidence: 99%

medigan: a Python library of pretrained generative models for medical image synthesis

et al. 2023

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Purpose: Deep learning has shown great promise as the backbone of clinical decision support systems. Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models. However, there is (1) limited availability of (synthetic) datasets and (2) generative models are complex to train, which hinders their adoption in research and clinical applications. To reduce this entry barrier, we explore generative model sharing to allow more researchers to access, generate, and benefit from synthetic data.Approach: We propose medigan, a one-stop shop for pretrained generative models implemented as an open-source framework-agnostic Python library. After gathering end-user requirements, design decisions based on usability, technical feasibility, and scalability are formulated. Subsequently, we implement medigan based on modular components for generative model (i) execution, (ii) visualization, (iii) search & ranking, and (iv) contribution. We integrate pretrained models with applications across modalities such as mammography, endoscopy, x-ray, and MRI. Results:The scalability and design of the library are demonstrated by its growing number of integrated and readily-usable pretrained generative models, which include 21 models utilizing nine different generative adversarial network architectures trained on 11 different datasets. We further analyze three medigan applications, which include (a) enabling community-wide sharing of restricted data, (b) investigating generative model evaluation metrics, and (c) improving clinical downstream tasks. In (b), we extract Fréchet inception distances (FID) demonstrating FID variability based on image normalization and radiology-specific feature extractors.Conclusion: medigan allows researchers and developers to create, increase, and domain-adapt their training data in just a few lines of code. Capable of enriching and accelerating the development of clinical machine learning models, we show medigan's viability as platform for generative model sharing. Our multimodel synthetic data experiments uncover standards for assessing and reporting metrics, such as FID, in image synthesis studies.

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

Cited by 109 publications

References 21 publications

FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

FuseMedML: a framework for accelerated discovery in machine learning based biomedicine

A Review of the Role of Causality in Developing Trustworthy AI Systems

medigan: a Python library of pretrained generative models for medical image synthesis

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