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13 search results found
Monai Deploy
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87
MONAI Deploy aims to become the de-facto standard for developing, packaging, testing, deploying and running medical AI applications in clinical production.
Projectaiai
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86
AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charitable hospitals, and organizations like WHO 🌏 We will also release our pretrained models and weights as Medical Imagenet.
Monai Deploy App Sdk
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74
MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.
Dicom_rt_and_images_to_mask
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70
Various utilities created to help with the interpretation of DICOM images and DICOM RT Structures to NumPy masks and Simple-ITK handles/Nifti images. Some necessary steps for deep learning
Mercure
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46
mercure DICOM Orchestrator
Pyradise
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37
PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion
Oncoserve_public
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27
Sharing Deep Learning Models for Breast Cancer Risk
Dicom Cnn
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26
Convolutional neural network solution for deep learning from DICOM images
Awesome Medical Imaging
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26
Awesome list of software that I use to do research in medical imaging.
Med Imagetools
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22
Transparent and reproducible medical image processing pipelines in Python.
Pyvoxel
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19
Fast Pythonic data structures and tools for wrangling medical images.
Innereye Createdataset
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14
InnerEye dataset creation tool for InnerEye-DeepLearning library. Transforms DICOM data into mask for training Deep Learning models.
Delia
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12
DICOM Extraction for Large-scale Image Analysis (DELIA).
Radtorch
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11
Official repository for RADTorch - The Medical Imaging Machine Learning Framework
Nnu Net Bpr Annotations
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6
Code accompanying this dataset: Krishnaswamy, D., Bontempi, D., Clunie, D., Aerts, H. & Fedorov, A. AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections. (2022). doi:10.5281/zenodo.7473970
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