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Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications.
Domain-specific language model pretraining for biomedical natural language processing (2020)
Gu, Y. et al · 2007
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Imagenet: A large-scale hierarchical image database
Deng, J. et al · 2009
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Few-shot medical image segmentation using a global correlation network with discriminative embedding (2020)
Sun, L. et al · 2012
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Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning
Shin, H.-C. et al · 2016
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Wang, X. et al · 2017
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I. et al · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C. & Kittler, H · 2018
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A baseline for few-shot image classification
Dhillon, G. S., Chaudhari, P., Ravichandran, A. & Soatto, S · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Irvin, J. A. et al · 2019
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Mimic-cxr: A large publicly available database of labeled chest radiographs
Johnson, A. E. W. et al · 2019
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Language models are few-shot learners
Brown, T. et al · 2020
Cited alongside, same era.
Rethinking few-shot image classification: a good embedding is all you need?
Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B. & Isola, P · 2020
Cited alongside, same era.
Self-supervision with superpixels: Training few-shot medical image segmentation without annotation
Ouyang, C. et al · 2020
Cited alongside, same era.
Meta-baseline: Exploring simple meta-learning for few-shot learning
Chen, Y., Liu, Z., Xu, H., Darrell, T. & Wang, X · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A. et al · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A. et al · 2021
Cited alongside, same era.
Learning to prompt for vision-language models
Zhou, K., Yang, J., Loy, C. C. & Liu, Z · 2022
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Conditional prompt learning for vision-language models
Zhou, K., Yang, J., Loy, C. C. & Liu, Z · 2022
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Masked autoencoders are scalable vision learners
He, K. et al · 2022
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Jia, M. et al · 2022
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Medical image understanding with pretrained vision language models: A comprehensive study
Qin, Z., Yi, H., Lao, Q. & Li, K · 2022
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Fhist: A benchmark for few-shot classification of histological images (2022)
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Metamed: Few-shot medical image classification using gradient-based meta-learning
Singh, R. et al · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Caron, M. et al · 2021
Cited alongside, same era.
The medical segmentation decathlon
Antonelli, M. et al · 2021
Cited alongside, same era.
Dicom anonymizer
the RSNA MIRC project
Cited in the paper.
https://github.com/open-mmlab/mmclassification
Mmclassification
Cited in the paper.
Shakeri, F. et al · 2022
Later among the works it cites.
Digestpath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system
Da, Q. et al · 2022
Later among the works it cites.
Simmim: A simple framework for masked image modeling
Xie, Z. et al · 2022
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A real-world dataset and benchmark for foundation1 model adaptation in medical image classification
Wang, D. et al · 2023
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