Radiologic Deep Learning Research and Commercial Development Datasets

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Now there is a new way to train medical image AI.

GENERATIVE IMAGES

RadImageNet is a large database of annotated medical images from multiple modalities and of multiple pathologies. The data can be licensed for commercial use.

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Over 1 million studies from over 500,000 verified patients.

STUDIES

The RIN key image dataset is comprised of 5 million labeled images of medical pathology with DICOM tags on over 1 million PET, CT, Ultrasound and MRI studies from 500,000 patients.

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Recent Publications

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Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data

For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of I...

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Artificial intelligence-enabled rapid diagnosis of patients with COVID-19

For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT–PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT–PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19.

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Automated machine learning-based radiomics analysis versus deep learning-based classification for thyroid nodule on ultrasound images: a multi-center study

Often, the characteristics of thyroid nodules need to be determined by fine needle aspiration (FNA) biopsy. The increasing applications of machine learning and deep learning algorithms provide alternative noninvasive methods to study thyroid nodules on ultrasound images.

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