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Neural networks pre-trained on a self-supervision scheme have become the standard when operating in data rich environments with scarce annotations.
Language models are few-shot learners
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Prototypical networks for few-shot learning
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Attention is all you need
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Automatic multi-organ segmentation on abdominal ct with dense v-networks
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Ct-org: Ct volumes with multiple organ segmentations
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nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
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Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A., 2020 · 2020
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A simple framework for contrastive learning of visual representations, in: International conference on machine learning, PMLR. pp. 1597–1607
Chen, T., Kornblith, S., Norouzi, M., Hinton, G., 2020 · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.B., Strub, F., Altch’e, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.Á., Guo, Z.D., Azar, M.G., Piot, B., Kavukcuoglu, K., Munos, R., Valko, M., 2020 · 2020
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A survey of the recent architectures of deep convolutional neural networks
Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S., 2020 · 2020
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Monai: Medical open network for ai
MONAI Consortium, 2020 · 2020
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Self-supervision with superpixels: Training few-shot medical image segmentation without annotation, in: European Conference on Computer Vision, Springer. pp. 762–780
Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D., 2020 · 2020
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Segmenter: Transformer for semantic segmentation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7262–7272
Strudel, R., Garcia, R., Laptev, I., Schmid, C., 2021 · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Zaken, E.B., Ravfogel, S., Goldberg, Y., 2021 · 2021
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Models genesis
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Data2vec: A general framework for self-supervised learning in speech, vision and language
Baevski, A., Hsu, W.N., Xu, Q., Babu, A., Gu, J., Auli, M., 2022 · 2022
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Bahng, H., Jahanian, A., Sankaranarayanan, S., Isola, P., 2022 · 2022
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Palm: Scaling language modeling with pathways
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Self-supervised learning from 100 million medical images
Ghesu, F.C., Georgescu, B., Mansoor, A., Yoo, Y., Neumann, D., Patel, P.B., Vishwanath, R.S., Balter, J.M., Cao, Y., Grbic, S., Comaniciu, D., 2022 · 2022
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Dilated neighborhood attention transformer
Hassani, A., Shi, H., 2022 · 2022
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Neighborhood attention transformer
Hassani, A., Walton, S., Li, J., Li, S., Shi, H., 2022 · 2022
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Unetr: Transformers for 3d medical image segmentation, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584
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Object discovery and representation networks
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