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Self-supervised pre-training appears as an advantageous alternative to supervised pre-trained for transfer learning.
Gutmann, M., Hyvärinen, A.: Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In: International Conference on Artificial Intelligence and Statistics (AISTATS) (2010)
2010
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Earlier work this paper cites.
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European Conference on Computer Vision (ECCV) (2016)
2016
Earlier work this paper cites.
Jamaludin, A., Kadir, T., Zisserman, A.: Self-supervised learning for spinal mris. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 294–302. Springer (2017)
2017
Earlier work this paper cites.
Menegola, A., Fornaciali, M., Pires, R., Bittencourt, F.V., Avila, S., Valle, E.: Knowledge transfer for melanoma screening with deep learning. In: International Symposium on Biomedical Imaging (ISBI) (2017)
2017
Earlier work this paper cites.
Codella, N., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., et al.: Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: International Symposium on Biomedical Imaging (ISBI) (2018)
2018
Earlier work this paper cites.
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: International Conference on Learning Representations (ICML) (2018)
2018
Earlier work this paper cites.
Hervella, Á.S., Rouco, J., Novo, J., Ortega, M.: Retinal image understanding emerges from self-supervised multimodal reconstruction. In: Medical Image Computing and Computer Assisted Intervention (MICCAI) (2018)
2018
Earlier work this paper cites.
Liu, X., Sinha, A., Unberath, M., Ishii, M., Hager, G.D., Taylor, R.H., Reiter, A.: Self-supervised learning for dense depth estimation in monocular endoscopy. In: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, pp. 128–138. Springer (2018)
2018
Earlier work this paper cites.
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Earlier work this paper cites.
Bai, W., Chen, C., Tarroni, G., Duan, J., Guitton, F., Petersen, S.E., Guo, Y., Matthews, P.M., Rueckert, D.: Self-supervised learning for cardiac mr image segmentation by anatomical position prediction. In: Medical Image Computing and Computer Assisted Intervention (MICCAI) (2019)
2019
Earlier work this paper cites.
Bissoto, A., Fornaciali, M., Valle, E., Avila, S.: (De)Constructing bias on skin lesion datasets. In: Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2019)
2019
Earlier work this paper cites.
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Medical Image Analysis 58
2019
Earlier work this paper cites.
Kawahara, J., Daneshvar, S., Argenziano, G., Hamarneh, G.: Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE Journal of Biomedical and Health Informatics 23
2019
Earlier work this paper cites.
Bissoto, A., Valle, E., Avila, S.: Debiasing skin lesion datasets and models? Not so fast. In: Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020)
2020
Earlier work this paper cites.
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Earlier work this paper cites.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (ICML) (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Geirhos, R., Jacobsen, J.H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., Wichmann, F.A.: Shortcut learning in deep neural networks. Nature Machine Intelligence 2
2020
Cited alongside, same era.
Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., Kavukcuoglu, K., Munos, R., Valko, M.: Bootstrap your own latent – A new approach to self-supervised learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Cited alongside, same era.
Zhou, H.Y., Yu, S., Bian, C., Hu, Y., Ma, K., Zheng, Y.: Comparing to learn: Surpassing imagenet pretraining on radiographs by comparing image representations. In: Medical Image Computing and Computer Assisted Intervention (MICCAI) (2020)
2020
Later among the works it cites.
Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., et al.: Big self-supervised models advance medical image classification. In: International Conference on Computer Vision (ICCV) (2021)
2021
Closest in time.
Boyd, J., Liashuha, M., Deutsch, E., Paragios, N., Christodoulidis, S., Vakalopoulou, M.: Self-supervised representation learning using visual field expansion on digital pathology. In: International Conference on Computer Vision (ICCV) (2021)
2021
Closest in time.
Chen, X., Yao, L., Zhou, T., Dong, J., Zhang, Y.: Momentum contrastive learning for few-shot covid-19 diagnosis from chest ct images. Pattern recognition 113
2021
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He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Cited alongside, same era.
Hu, D., Qian, R., Jiang, M., Tan, X., Wen, S., Ding, E., Lin, W., Dou, D.: Discriminative sounding objects localization via self-supervised audiovisual matching. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 33 (2020)
2020
Cited alongside, same era.
Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)
2020
Cited alongside, same era.
Kawakami, K., Wang, L., Dyer, C., Blunsom, P., van den Oord, A.: Learning robust and multilingual speech representations. In: Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
2020
Cited alongside, same era.
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised contrastive learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Cited alongside, same era.
Li, Y., Chen, J., Zheng, Y.: A multi-task self-supervised learning framework for scopy images. In: International Symposium on Biomedical Imaging (ISBI) (2020)
2020
Cited alongside, same era.
Pacheco, A.G., Lima, G.R., Salomão, A.S., Krohling, B., Biral, I.P., de Angelo, G.G., Alves Jr, F.C., Esgario, J.G., Simora, A.C., Castro, P.B., et al.: Pad-ufes-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones. Data in brief 32
2020
Cited alongside, same era.
Srikar Appalaraju, Yi Zhu, Y.X., Fehervari, I.: Towards good practices in self-supervised representation learning. In: Advances in Neural Information Processing Systems Workshops (NeurIPSW) (2020)
2020
Cited alongside, same era.
Closest in time.
Hosseinzadeh Taher, M.R., Haghighi, F., Feng, R., Gotway, M.B., Liang, J.: A systematic benchmarking analysis of transfer learning for medical image analysis. In: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health, pp. 3–13. Springer (2021)
2021
Closest in time.
Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., Tang, J.: Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering (2021)
2021
Closest in time.
Morís, D.I., Hervella, Á.S., Rouco, J., Novo, J., Ortega, M.: Context encoder self-supervised approaches for eye fundus analysis. In: International Joint Conference on Neural Networks (IJCNN) (2021)
2021
Closest in time.
Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Caffery, L., Chousakos, E., Codella, N., Combalia, M., Dusza, S., Guitera, P., Gutman, D., et al.: A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Scientific data 8
2021
Closest in time.
2021
Closest in time.
Truong, T., Mohammadi, S., Lenga, M.: How transferable are self-supervised features in medical image classification tasks? In: Machine Learning for Health. pp. 54–74. PMLR (2021)
2021
Closest in time.
Vu, Y.N.T., Wang, R., Balachandar, N., Liu, C., Ng, A.Y., Rajpurkar, P.: Medaug: Contrastive learning leveraging patient metadata improves representations for chest x-ray interpretation. In: Machine Learning for Healthcare Conference. pp. 755–769 (2021)
2021
Closest in time.
Wang, D., Pang, N., Wang, Y., Zhao, H.: Unlabeled skin lesion classification by self-supervised topology clustering network. Biomedical Signal Processing and Control 66
2021
Closest in time.
2022
Closest in time.
Cole, E., Yang, X., Wilber, K., Mac Aodha, O., Belongie, S.: When does contrastive visual representation learning work? In: Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Closest in time.
Verdelho, M.R., Barata, C.: On the impact of self-supervised learning in skin cancer diagnosis. In: International Symposium on Biomedical Imaging (2022)
2022
Closest in time.
Wang, Z., Lyu, J., Luo, W., Tang, X.: Superpixel inpainting for self-supervised skin lesion segmentation from dermoscopic images. In: International Symposium on Biomedical Imaging (ISBI) (2022)
2022
Closest in time.