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Masked Autoencoder (MAE) has recently been shown to be effective in pre-training Vision Transformers (ViT) for natural image analysis.
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Olaf Ronneberger et al., · 2015
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“Attention is all you need,”
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“Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning,”
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“Chestnet: A deep neural network for classification of thoracic diseases on chest radiography,”
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“Attention gated networks: Learning to leverage salient regions in medical images,”
Jo Schlemper et al., · 2019
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“Pytorch: An imperative style, high-performance deep learning library,”
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“An image is worth 16x16 words: Transformers for image recognition at scale,”
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“MONAI: Medical Open Network for AI,” 3 2020
MONAI Consortium, · 2020
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Hong-Yu Zhou et al., · 2021
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“nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,”
Fabian Isensee et al., · 2021
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“Chest radiograph disentanglement for covid-19 outcome prediction,”
Lei Zhou et al., · 2021
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“Predicting mechanical ventilation and mortality in covid-19 using radiomics and deep learning on chest radiographs: A multi-institutional study,”
Joseph Bae et al., · 2021
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“Masked autoencoders are scalable vision learners,”
Kaiming He et al., · 2022
Closest in time.
“Unetr: Transformers for 3d medical image segmentation,”
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Hangbo Bao et al., · 2021
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“The medical segmentation decathlon,”
Michela Antonelli et al., · 2021
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“An empirical study of training self-supervised vision transformers,”
Xinlei Chen et al., · 2021
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“Transunet: Transformers make strong encoders for medical image segmentation,”
Jieneng Chen et al., · 2021
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“Transunet,”
Jie-Neng Chen,
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Lei Zhou et al., · 2022
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“Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,”
Xiaosong Wang et al., · 2097
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