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Based on digital pathology slice scanning technology, artificial intelligence algorithms represented by deep learning have achieved remarkable results in the field of computational pathology.
Contrastive learning of medical visual representations from paired images and text
Zhang, Y., Jiang, H., Miura, Y., Manning, C.D., Langlotz, C.P., 2020 · 2010
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Self-supervision closes the gap between weak and strong supervision in histology
Dehaene, O., Camara, A., Moindrot, O., de Lavergne, A., Courtiol, P., 2020 · 2012
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Diagnostic concordance among pathologists interpreting breast biopsy specimens
Elmore, J.G., Longton, G.M., Carney, P.A., Geller, B.M., Onega, T., Tosteson, A.N., Nelson, H.D., Pepe, M.S., Allison, K.H., Schnitt, S.J., et al., 2015 · 2015
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Zhuang, F., Cheng, X., Luo, P., Pan, S.J., He, Q., 2015 · 2015
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Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
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Deep learning for identifying metastatic breast cancer
Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H., 2016 · 2016
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B., Sánchez, C.I., 2017 · 2017
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An overview of multi-task learning in deep neural networks
Ruder, S., 2017 · 2017
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100,000 histological images of human colorectal cancer and healthy tissue
Kather, J.N., Halama, N., Marx, A., 2018 · 2018
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Unsupervised feature learning via non-parametric instance discrimination, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3733–3742
Wu, Z., Xiong, Y., Yu, S.X., Lin, D., 2018 · 2018
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M.G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K.J., Brogi, E., Reuter, V.E., Klimstra, D.S., Fuchs, T.J., 2019 · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A., 2020 · 2020
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Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9729–9738
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R., 2020 · 2020
Cited alongside, same era.
Big transfer (bit): General visual representation learning, in: European conference on computer vision, Springer. pp. 491–507
Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., Houlsby, N., 2020 · 2020
Cited alongside, same era.
Data-efficient histopathology image analysis with deformation representation learning, in: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE. pp. 857–864
Xu, J., Hou, J., Zhang, Y., Feng, R., Ruan, C., Zhang, T., Fan, W., 2020 · 2020
Cited alongside, same era.
Transfer learning: Survey and classification
Agarwal, N., Sondhi, A., Chopra, K., Singh, G., 2021 · 2021
Cited alongside, same era.
Densecapsnet: Detection of covid-19 from x-ray images using a capsule neural network
Quan, H., Xu, X., Zheng, T., Li, Z., Zhao, M., Cui, X., 2021 · 2021
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Selfaugment: Automatic augmentation policies for self-supervised learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2674–2683
Reed, C.J., Metzger, S., Srinivas, A., Darrell, T., Keutzer, K., 2021 · 2021
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Transpath: Transformer-based self-supervised learning for histopathological image classification, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VIII 24, Springer. pp. 186–195
Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Huang, J., Yang, W., Han, X., 2021 · 2021
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Self-supervised visual representation learning for histopathological images, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 47–57
Yang, P., Hong, Z., Yin, X., Zhu, C., Jiang, R., 2021 · 2021
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Bao, H., Dong, L., Wei, F., 2021 · 2021
Cited alongside, same era.
Self-supervised representation learning using visual field expansion on digital pathology, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 639–647
Boyd, J., Liashuha, M., Deutsch, E., Paragios, N., Christodoulidis, S., Vakalopoulou, M., 2021 · 2021
Cited alongside, same era.
Momentum contrastive learning for few-shot covid-19 diagnosis from chest ct images
Chen, X., Yao, L., Zhou, T., Dong, J., Zhang, Y., 2021 · 2021
Cited alongside, same era.
Applying self-supervised learning to medicine: review of the state of the art and medical implementations, in: Informatics, MDPI. p. 59
Chowdhury, A., Rosenthal, J., Waring, J., Umeton, R., 2021 · 2021
Cited alongside, same era.
Sslp: Spatial guided self-supervised learning on pathological images, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 3–12
Li, J., Lin, T., Xu, Y., 2021 · 2021
Cited alongside, same era.
Self-supervised learning: Generative or contrastive
Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., Tang, J., 2021 · 2021
Cited alongside, same era.
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F., 2021 · 2021
Cited alongside, same era.
Reducing annotation effort in digital pathology: A co-representation learning framework for classification tasks
Pati, P., Foncubierta-Rodríguez, A., Goksel, O., Gabrani, M., 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Context autoencoder for self-supervised representation learning
Chen, X., Ding, M., Wang, X., Xin, Y., Mo, S., Wang, Y., Han, S., Luo, P., Zeng, G., Wang, J., 2022 · 2022
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Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R., 2022 · 2022
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Self-distillation augmented masked autoencoders for histopathological image classification
Luo, Y., Chen, Z., Gao, X., 2022 · 2022
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Self-supervised learning methods and applications in medical imaging analysis: A survey
Shurrab, S., Duwairi, R., 2022 · 2022
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Self-supervised driven consistency training for annotation efficient histopathology image analysis
Srinidhi, C.L., Kim, S.W., Chen, F.D., Martel, A.L., 2022 · 2022
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Masked feature prediction for self-supervised visual pre-training, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14668–14678
Wei, C., Fan, H., Xie, S., Wu, C.Y., Yuille, A., Feichtenhofer, C., 2022 · 2022
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Self pre-training with masked autoencoders for medical image analysis
Zhou, L., Liu, H., Bae, J., He, J., Samaras, D., Prasanna, P., 2022 · 2022
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