LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Google Scholar
Yala, A., Lehman, C., Schuster, T., Portnoi, T. & Barzilay, R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292, 60–66 (2019).
Google Scholar
Wu, N. et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging 39, 1184–1194 (2019).
Google Scholar
McKinney, S. M. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020).
Google Scholar
Rajpurkar, P. et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 15, e1002686 (2018).
Google Scholar
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
Google Scholar
Liu, Y. et al. A deep learning system for differential diagnosis of skin diseases. Nat. Med. 26, 900–908 (2020).
Google Scholar
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16, 703–715 (2019).
Google Scholar
Rakha, E. A. et al. Current and future applications of artificial intelligence in pathology: a clinical perspective. J. Clin. Pathol. 74, 409–414 (2021).
Google Scholar
Wulczyn, E. et al. Interpretable survival prediction for colorectal cancer using deep learning. npj Digit. Med. 4, 71 (2021).
Google Scholar
Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).
Google Scholar
De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
Google Scholar
Zhou, S. K. et al. A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proc. IEEE 109, 820–838 (2021).
Google Scholar
Condon, J. J. J. et al. Replication of an open-access deep learning system for screening mammography: reduced performance mitigated by retraining on local data. Preprint at medRxiv https://doi.org/10.1101/2021.05.28.21257892 (2021).
Zech, J. R. et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 15, e1002683 (2018).
Google Scholar
Zhang, H. et al. An empirical framework for domain generalization in clinical settings. In Proc. Conference on Health, Inference, and Learning (eds Ghassemi, M. et al.) 279–290 (Association for Computing Machinery, 2021).
Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I. Y. & Ghassemi, M. CheXclusion: fairness gaps in deep chest X-ray classifiers. Pac. Symp. Biocomput. 26, 232–243 (2021).
Google Scholar
Kadambi, A. Achieving fairness in medical devices. Science 372, 30–31 (2021).
Google Scholar
Pierson, E., Cutler, D. M., Leskovec, J., Mullainathan, S. & Obermeyer, Z. An algorithmic approach to reducing unexplained pain disparities in underserved populations. Nat. Med. 27, 136–140 (2021).
Artificial Intelligence in Health Care: Benefits and Challenges of Technologies to Augment Patient Care (US Government Accountability Office, 2020).
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019).
Google Scholar
Roberts, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021).
Google Scholar
Van Leeuwen, K. G., Schalekamp, S., Rutten, M. J., van Ginneken, B. & de Rooij, M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur. Radiol. 31, 3797–3804 (2021).
Google Scholar
Freeman, K. et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. bmj 374, n1872 (2021).
Google Scholar
D’Amour, A. et al. Underspecification presents challenges for credibility in modern machine learning. J. Mach. Learn. Res. 23, 1–61 (2020).
Finlayson, S. G. et al. The clinician and dataset shift in artificial intelligence. N. Engl. J. Med. 386, 283–286 (2020).
Futoma, J., Simons, M., Panch, T., Doshi-Velez, F. & Celi, L. A. The myth of generalisability in clinical research and machine learning in health care. Lancet Dig. Health 2, e489–e492 (2020).
Google Scholar
Willemink, M. J. et al. Preparing medical imaging data for machine learning. Radiology 295, 4–15 (2020).
Google Scholar
Li, F.-F., Fergus, R. & Perona, P. One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28, 594–611 (2006).
Google Scholar
Zhu, X., Ghahramani, Z. & Lafferty, J. D. Semi-supervised learning using gaussian fields and harmonic functions. In Proc. 20th International Conference on Machine Learning (eds Fawcett, T. & Mishra, N.) 912–919 (AAAI Press, 2003).
Cohn, D., Atlas, L. & Ladner, R. Improving generalization with active learning. Mach. Learn. 15, 201–221 (1994).
Google Scholar
Sutton, R. S. Generalization in reinforcement learning: successful examples using sparse coarse coding. Adv. Neural Inf. Process. Syst. 8, 1038–1044 (1996).
Doersch, C., Gupta, A. & Efros, A. A. Unsupervised visual representation learning by context prediction. In Proc. IEEE International Conference on Computer Vision 1422–1430 (IEEE, 2015).
Doersch, C. & Zisserman, A. Multi-task self-supervised visual learning. In Proc. IEEE International Conference on Computer Vision 2070–2079 (IEEE, 2017).
Gidaris, S., Singh, P. & Komodakis, N. Unsupervised representation learning by predicting image rotations. Preprint at https://arxiv.org/abs/1803.07728 (2018).
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T. & Efros, A. A. Context encoders: Feature learning by inpainting. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2536–2544 (IEEE, 2016).
Larsson, G., Maire, M. & Shakhnarovich, G. Colorization as a proxy task for visual understanding. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 6874–6883 (IEEE, 2017).
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: pre-training of deep bidirectional transformers for language understanding. Preprint at https://arxiv.org/abs/1810.04805 (2018).
Brown, T. B. et al. Language models are few-shot learners. Adv. Neural Inf. Process Syst. 33, 1877–1901 (2020).
Baevski, A., Auli, M. & Mohamed, A. Effectiveness of self-supervised pre-training for speech recognition. Preprint at https://arxiv.org/abs/1911.03912 (2019).
Chen, L. et al. Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (2019).
Google Scholar
He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 9729–9738 (IEEE, 2020).
Grill, J.-B. et al. Bootstrap your own latent: a new approach to self-supervised learning. Adv. Neural Inf. Process. Syst. 33, 21271–21284 (2020).
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In Proc. 37th International Conference on Machine Learning (eds Daumé, H. & Singh, A.) 1597–1607 (JMLR, 2020).
Deng, J. et al. Imagenet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016).
Touvron, H. et al. Training data-efficient image transformers and distillation through attention. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 10347–10357 (PMLR, 2021).
Liu, H. & Abbeel, P. Hybrid discriminative-generative training via contrastive learning. Preprint at https://arxiv.org/abs/2007.09070 (2020).
Winkens, J. et al. Contrastive training for improved out-of-distribution detection. Preprint at https://arxiv.org/abs/2007.05566 (2020).
Shen, K. et al. Connect, not collapse: explaining contrastive learning for unsupervised domain adaptation. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 19847–19878 (PMLR, 2022).
HaoChen, J. Z., Wei, C., Kumar, A. & Ma, T. Beyond separability: analyzing the linear transferability of contrastive representations to related subpopulations. Preprint at https://arxiv.org/abs/2204.02683 (2022).
Kolesnikov, A. et al. Big transfer (BiT): general visual representation learning. In Proc. European Conference on Computer Vision (eds Vedaldi, A. et al.) 491–507 (Springer, 2020).
Huh, M., Agrawal, P. & Efros, A. A. What makes ImageNet good for transfer learning? Preprint at https://arxiv.org/abs/1608.08614 (2016).
Sun, C., Shrivastava, A., Singh, S. & Gupta, A. Revisiting unreasonable effectiveness of data in deep learning era. In Proc. IEEE International Conference on Computer Vision 843–852 (IEEE, 2017).
Mahajan, D. et al. Exploring the limits of weakly supervised pretraining. In Proc. European Conference on Computer Vision (eds Ferrari, V. et al.) 185–201 (Springer, 2018).
Houlsby, N. & Zhai, X. The Visual Task Adaptation Benchmark (Google Research, 2019).
Mustafa, B. et al. Supervised transfer learning at scale for medical imaging. Preprint at https://arxiv.org/abs/2101.05913 (2021).
Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: understanding transfer learning for medical imaging. Adv. Neural Inf. Process. Syst. 33, 3347–3357 (2019).
Hendrycks, D., Lee, K. & Mazeika, M. Using pre-training can improve model robustness and uncertainty. In Proc. 36th International Conference on Machine Learning (eds Chaudhuri, K. & Salakhutdinov, R.) 2712–2721 (PMLR, 2019).
Li, J., Lin, T. & Xu, Y. SSLP: Spatial guided self-supervised learning on pathological images. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds de Bruijne, M. et al.) 3–12 (Springer, 2021).
Srinidhi, C. L. & Martel, A. L. Improving self-supervised learning with hardness-aware dynamic curriculum learning: an application to digital pathology. In Proc. IEEE/CVF International Conference on Computer Vision 562–571 (IEEE, 2021).
Azizi, S. et al. Big self-supervised models advance medical image classification. In IEEE/CVF International Conference on Computer Vision (ICCV) 3458–3468 (IEEE, 2021).
Sowrirajan, H., Yang, J., Ng, A. Y. & Rajpurkar, P. MoCo pretraining improves representation and transferability of chest X-ray models. In Proc. Fourth Conference on Medical Imaging with Deep Learning (eds Heinrich, M. et al.) 728–744 (PMLR, 2021).
Zhou, Z. et al. Models genesis: generic autodidactic models for 3D medical image analysis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Shen, D. et al.) 384–393 (2019).
Liu, X. et al. Self-supervised learning: generative or contrastive. IEEE Trans. Knowl. Data Eng. 35, 857–876 (2023).
Wang, X. et al. Chestx-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 3462–3471 (IEEE, 2017).
Hendrycks, D. et al. Pretrained transformers improve out-of-distribution robustness. Preprint at https://arxiv.org/abs/2004.06100 (2020).
Russakovsky, O. et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015).
Google Scholar
Alzubaidi, L. et al. Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model. Electronics 9, 445 (2020).
Google Scholar
Graziani, M., Andrearczyk, V. & Müller, H. Visualizing and interpreting feature reuse of pretrained CNNs for histopathology. In Proc. IMVIP 2019: Irish Machine Vision and Image Processing (Technological University Dublin, 2019).
Wu, Y. & He, K. Group normalization. In Proc. European Conference on Computer Vision (ECCV) 3–19 (2018).
Chen, T., Kornblith, S., Swersky, K., Norouzi, M. & Hinton, G. Big self-supervised models are strong semi-supervised learners. Adv. Neural Inf. Process. Syst. 33, 22243–22255 (2020).
Becker, S. & Hinton, G. E. Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature 355, 161–163 (1992).
Google Scholar
Virgili, G. et al. Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy. Cochrane Database Syst. Rev. 1, CD008081 (2015).
Google Scholar
Liu, X. et al. Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study. Ophthalmol. Retina 6, 398–410 (2022).
Google Scholar
Brown, J. C. et al. Detection of diabetic foveal edema: contact lens biomicroscopy compared with optical coherence tomography. Arch. Ophthalmol. 122, 330–335 (2004).
Google Scholar
Sadda, S. R. et al. Automated detection of clinically significant macular edema by grid scanning optical coherence tomography. Ophthalmology 113, 1187.e1-12 (2006).
Google Scholar
Irvin, J. et al. Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proc. Conf. AAAI Artif. Intell. 33, 590–597 (2019).
Johnson, A. E. et al. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci. Data 6, 317 (2019).
Google Scholar
Neyshabur, B., Sedghi, H. & Zhang, C. What is being transferred in transfer learning? Adv. Neural Inf. Process. Syst. 33, 512–523 (2020).
Ilse, M., Tomczak, J. & Welling, M. Attention-based deep multiple instance learning. In Proc. 35th International Conference on Machine Learning (eds Dy, J. & Krause, A.) 2127–2136 (PMLR, 2018).
Bejnordi, B. E. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017).
Google Scholar
Vu, Y. N. T. et al. MedAug: contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation. In Proc. 6th Machine Learning for Healthcare Conference (eds Jung, K. et al.) 755–769 (PMLR, 2021).
Chen, X., Fan, H., Girshick, R. & He, K. Improved baselines with momentum contrastive learning. Preprint at https://arxiv.org/abs/2003.04297 (2020).
Mitrovic, J., McWilliams, B., Walker, J., Buesing, L. & Blundell, C. Representation learning via invariant causal mechanisms. Preprint at https://arxiv.org/abs/2010.07922 (2020).
Zbontar, J., Jing, L., Misra, I., LeCun, Y. & Deny, S. Barlow twins: self-supervised learning via redundancy reduction. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 12310–12320 (PMLR, 2021).
Dunnmon, J. A. et al. Cross-modal data programming enables rapid medical machine learning. Patterns 1, 100019 (2020).
Google Scholar
Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).
Google Scholar
Eyuboglu, S. et al. Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT. Nat. Commun. 12, 1880 (2021).
Google Scholar
Bakalo, R., Ben-Ari, R. & Goldberger, J. Classification and detection in mammograms with weak supervision via dual branch deep neural net. In IEEE 16th International Symposium on Biomedical Imaging (ISBI) 1905–1909 (IEEE, 2019).
Wenzel, F. et al. Assaying out-of-distribution generalization in transfer learning. Adv. Neural Inf. Process. Syst. 35, 7181–7198 (2022).
Hendrycks, D. & Dietterich, T. Benchmarking neural network robustness to common corruptions and perturbations. Preprint at https://arxiv.org/abs/1903.12261 (2019).
Wang, Z., Dai, Z., Póczos, B. & Carbonell, J. Characterizing and avoiding negative transfer. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 11285–11294 (IEEE, 2019).
Gulrajani, I. & Lopez-Paz, D. In search of lost domain generalization. Preprint at https://arxiv.org/abs/2007.01434 (2020).
Vapnik, V. N. Statistical Learning Theory (Wiley-Interscience, 1998).
Zhang, H., Cisse, M., Dauphin, Y. N. & Lopez-Paz, D. mixup: beyond empirical risk minimization. Preprint at https://arxiv.org/abs/1710.09412 (2017).
Goyal, P. et al. Self-supervised pretraining of visual features in the wild. Preprint at https://arxiv.org/abs/2103.01988 (2021).
Bubeck, S. & Sellke, M. A universal law of robustness via isoperimetry. J. ACM 70, 1–18 (2023).
Google Scholar
Ericsson, L., Gouk, H. & Hospedales, T. M. How well do self-supervised models transfer? In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 5410–5419 (IEEE, 2021).
Chen, X. & He, K. Exploring simple Siamese representation learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 15745–15753 (IEEE, 2021).
Ciga, O., Martel, A. L. & Xu, T. Self-supervised contrastive learning for digital histopathology. Mach. Learn. 7, 100198 (2022).
Taher, M. R. H., Haghighi, F., Gotway, M. B. & Liang, J. CAiD: context-aware instance discrimination for self-supervised learning in medical imaging. In Proc. 5th International Conference on Medical Imaging with Deep Learning (eds Konukoglu, E. et al.) 535–551 (PMLR, 2022).
Taher, M. R. H., Haghighi, F., Feng, R., Gotway, M. B. & Liang, J. in Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health (eds Albarqouni, S. et al.) 3–13 (Springer, 2021).
Xie, Q., Luong, M.-T., Hovy, E. & Le, Q. V. Self-training with noisy student improves imagenet classification. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 10684–10695 (IEEE, 2020).
Srinidhi, C. L., Kim, S. W., Chen, F.-D. & Martel, A. L. Self-supervised driven consistency training for annotation efficient histopathology image analysis. Med. Image Anal. 75, 102256 (2022).
Google Scholar
Li, Z. et al. Domain generalization for mammography detection via multi-style and multi-view contrastive learning. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds de Bruijne, M. et al.) 98–108 (Springer, 2021).
Sato, J. et al. Anatomy-aware self-supervised learning for anomaly detection in chest radiographs. Preprint at https://arxiv.org/abs/2205.04282 (2022).
Wortsman, M. et al. Robust fine-tuning of zero-shot models. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 7959–7971 (IEEE, 2022).
Nguyen, T., Raghu, M. & Kornblith, S. Do wide and deep networks learn the same things? Uncovering how neural network representations vary with width and depth. Preprint at https://arxiv.org/abs/2010.15327 (2020).
Dosovitskiy, A. et al. An image is worth 16×16 words: transformers for image recognition at scale. In International Conference on Learning Representations (ICLR) (OpenReview, 2021).
He, K., Zhang, X., Ren, S. & Sun, J. Identity mappings in deep residual networks. In European Conference on Computer Vision (eds Leibe, B. et al.) 630–645 (Springer, 2016).
Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proc. 32nd International Conference on Machine Learning (eds Bach, F. & Blei, D.) 448–456 (2015).
Qiao, S., Wang, H., Liu, C., Shen, W. & Yuille, A. Micro-batch training with batch-channel normalization and weight standardization. Preprint at https://arxiv.org/abs/1903.10520 (2019).
You, Y., Gitman, I. & Ginsburg, B. Large batch training of convolutional networks. Preprint at https://arxiv.org/abs/1708.03888 (2017).
Castro, E., Cardoso, J. S. & Pereira, J. C. Elastic deformations for data augmentation in breast cancer mass detection. In IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) 230–234 (IEEE, 2018).
Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Navab, N. et al.) 234–241 (Springer, 2015).
Szegedy, C. et al. Going deeper with convolutions. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 1–9 (IEEE, 2015).
Tripuraneni, N., Jordan, M. I. & Jin, C. On the theory of transfer learning: the importance of task diversity. Adv. Neural Inf. Process. Syst. 33, 7852–7862 (2020).
Du, S. S., Hu, W., Kakade, S. M., Lee, J. D. & Lei, Q. Few-shot learning via learning the representation, provably. Preprint at https://arxiv.org/abs/2002.09434 (2020).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
Loshchilov, I. & Hutter, F. Sgdr: stochastic gradient descent with warm restarts. Preprint at https://arxiv.org/abs/1608.03983 (2016).
Goyal, P. et al. Accurate, large minibatch sgd: training imagenet in 1 hour. Preprint at https://arxiv.org/abs/1706.02677 (2017).
Bengio, Y., Goodfellow, I. & Courville, A. Deep Learning (MIT Press, 2017).
Wang, M. & Deng, W. Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018).
Google Scholar
Bello, I. et al. Revisiting resnets: improved training and scaling strategies. Adv. Neural Inf. Process. Syst. 34, 22614–22627 (2021).
Varadarajan, A. V. et al. Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning. Nat. Commun. 11, 130 (2020).
Google Scholar
Winkler, J. K. et al. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol. 155, 1135–1141 (2019).
Google Scholar
Seah, J. C. et al. Effect of a comprehensive deep-learning model on the accuracy of chest X-ray interpretation by radiologists: a retrospective, multireader multicase study. Lancet Digit. Health 3, e496–e506 (2021).
Google Scholar
Haygood, T. M. et al. Timed efficiency of interpretation of digital and film-screen screening mammograms. AJR Am. J. Roentgenol. 192, 216–220 (2009).
Google Scholar
Jain, A. et al. Development and assessment of an artificial intelligence–based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices. JAMA Netw. Open 4, e217249 (2021).
Google Scholar
Pugh, J. A. et al. Screening for diabetic retinopathy: the wide-angle retinal camera. Diabetes Care 16, 889–895 (1993).
Google Scholar
Schölkopf, B. et al. Toward causal representation learning. Proc. IEEE 109, 612–634 (2021).
Google Scholar
Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013).
Google Scholar
Liu, J., Hu, Z., Cui, P., Li, B. & Shen, Z. Heterogeneous risk minimization. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T) 6804–6814 (PMLR, 2021).
Robey, A., Pappas, G. J. & Hassani, H. Model-based domain generalization. Adv. Neural Inf. Process. Syst. 34, 20210–20229 (2021).
Shen, Z. et al. Towards out-of-distribution generalization: a survey. Preprint at https://arxiv.org/abs/2108.13624 (2021).
Wang, J. et al. Generalizing to unseen domains: a survey on domain generalization. IEEE Trans. Knowl. Data Eng. (2022).
Zhou, K., Liu, Z., Qiao, Y., Xiang, T. & Loy, C. C. Domain generalization: a survey. Preprint at https://arxiv.org/abs/2103.02503 (2021).
Locatello, F. et al. Challenging common assumptions in the unsupervised learning of disentangled representations. In Proc. 36th International Conference on Machine Learning (eds Chaudhuri, K. & Salakhutdinov, R.) 4114–4124 (PMLR, 2019).
Geirhos, R. et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. Preprint at https://arxiv.org/abs/1811.12231 (2018).
Geirhos, R. et al. Generalisation in humans and deep neural networks. Adv. Neural Inf. Process. Syst. 31, 7538–7550 (2018).
Kim, H. & Mnih, A. Disentangling by factorising. In Proc. 35th International Conference on Machine Learning (eds Dy, J. & Krause, A.) 2649–2658 (PMLR, 2018).
Yang, M. et al. CausalVAE: disentangled representation learning via neural structural causal models. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 9588–9597 (IEEE, 2021).
Leeb, F. et al. Structure by architecture: disentangled representations without regularization. Preprint at https://arxiv.org/abs/2006.07796 (2020).
Träuble, F. et al. On disentangled representations learned from correlated data. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 10401–10412 (PMLR, 2021).
Dittadi, A. et al. On the transfer of disentangled representations in realistic settings. Preprint at https://arxiv.org/abs/2010.14407 (2020).
Andreassen, A., Bahri, Y., Neyshabur, B. & Roelofs, R. The evolution of out-of-distribution robustness throughout fine-tuning. Preprint at https://arxiv.org/abs/2106.15831 (2021).
Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 8748–8763 (PMLR, 2021).
Taori, R. et al. When robustness doesn’t promote robustness: synthetic vs. natural distribution shifts on ImageNet. In International Conference on Learning Representations (ICLR) (2019).
Albuquerque, I., Monteiro, J., Darvishi, M., Falk, T. H. & Mitliagkas, I. Adversarial Target-Invariant Representation Learning for Domain Generalization (DeepAI, 2020).
Li, Y. et al. Deep domain generalization via conditional invariant adversarial networks. In Proc. European Conference on Computer Vision (ECCV) (eds Ferrari, V. et al.) 624–663 (Springer, 2018).
Ganin, Y. & Lempitsky, V. Unsupervised domain adaptation by backpropagation. In Proc. 32nd International Conference on Machine Learning (eds Bach, F. & Blei, D.) 1180–1189 (JMLR, 2015).
Ganin, Y. et al. Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17, 2096–2030 (2016).
Shao, R., Lan, X., Li, J. & Yuen, P. C. Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 10015–10023 (IEEE, 2019).
Motiian, S., Piccirilli, M., Adjeroh, D. A. & Doretto, G. Unified deep supervised domain adaptation and generalization. In Proc. IEEE International Conference on Computer Vision 5716–5726 (IEEE, 2017).
Muandet, K., Balduzzi, D. & Schölkopf, B. Domain generalization via invariant feature representation. In Proc. 30th International Conference on Machine Learning (eds Dasgupta, S. & McAllester, D.) I-10–I-18 (2013).
Menegola, A. et al. Knowledge transfer for melanoma screening with deep learning. In IEEE 14th International Symposium on Biomedical Imaging (ISBI) 297–300 (IEEE, 2017).
Xie, H. et al. Dual network architecture for few-view CT-trained on ImageNet data and transferred for medical imaging. In Proc. SPIE 11113, Developments in X-Ray Tomography XII (eds Müller, B. & Wang, G.) 111130V (SPIE, 2019).
Alzubaidi, L. et al. Towards a better understanding of transfer learning for medical imaging: a case study. Appl. Sci. 10, 4523 (2020).
Google Scholar
Heker, M. & Greenspan, H. Joint liver lesion segmentation and classification via transfer learning. Preprint at https://arxiv.org/abs/2004.12352 (2020).
Chen, S., Ma, K. & Zheng, Y. Med3D: transfer learning for 3D medical image analysis. Preprint at https://arxiv.org/abs/1904.00625 (2019).
Liang, G. & Zheng, L. A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Comput. Methods Prog. Biomed. 187, 104964 (2020).
Google Scholar
Geyer, R., Corinzia, L. & Wegmayr, V. Transfer learning by adaptive merging of multiple models. In Proc. 2nd International Conference on Medical Imaging with Deep Learning (eds Cardoso, M. J. et al.) 185–196 (PMLR, 2019).
Noroozi, M. & Favaro, P. Unsupervised learning of visual representations by solving jigsaw puzzles. In European Conference on Computer Vision (eds Leibe, B. et al.) 69–84 (Springer, 2016).
Zhang, R., Isola, P. & Efros, A. A. Colorful image colorization. In European Conference on Computer Vision (eds Leibe, B. et al.) 649–666 (Springer, 2016).
Wu, Z., Xiong, Y., Yu, S. X. & Lin, D. Unsupervised feature learning via non-parametric instance discrimination. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 3733–3742 (IEEE, 2018).
Hénaff, O. J. et al. Data-efficient image recognition with contrastive predictive coding. In Proc. 37th International Conference on Machine Learning (eds Daumé, H. & Singh, A.) 4182–4192 (PMLR, 2020).
van den Oord, A., Li, Y. & Vinyals, O. Representation learning with contrastive predictive coding. Preprint at https://arxiv.org/abs/1807.03748 (2018).
Hjelm, R. D. et al. Learning deep representations by mutual information estimation and maximization. Preprint at https://arxiv.org/abs/1808.06670v5 (2019).
Ye, M., Zhang, X., Yuen, P. C. & Chang, S.-F. Unsupervised embedding learning via invariant and spreading instance feature. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 6203–6212 (IEEE, 2019).
Bachman, P., Hjelm, R. D. & Buchwalter, W. Learning representations by maximizing mutual information across views. Adv. Neural Inf. Process. Syst. 15535–15545 (2019).
Tian, Y., Krishnan, D. & Isola, P. Contrastive multiview coding. In European Conference on Computer Vision (eds Vedaldi, A. et al.) 776–794 (Springer, 2019).
Misra, I. & Maaten, L. V. D. Self-supervised learning of pretext-invariant representations. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 6706–6716 (IEEE, 2020).
Caron, M. et al. Unsupervised learning of visual features by contrasting cluster assignments. Adv. Neural Inf. Process. Syst. 33, 9912–9924 (2020).
Bai, W. et al. Self-supervised learning for cardiac MR image segmentation by anatomical position prediction. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Shen, D. et al.) 541–549 (Springer, 2019).
Spitzer, H., Kiwitz, K., Amunts, K., Harmeling, S. & Dickscheid, T. Improving cytoarchitectonic segmentation of human brain areas with self-supervised Siamese networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Frangi, A. F. et al.) 663–671 (Springer, 2018).
Zhuang, X. et al. Self-supervised feature learning for 3D medical images by playing a Rubik’s cube. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Shen, D. et al.) 420–428 (Springer, 2019).
Zhu, J. et al. Rubik’s Cube+: a self-supervised feature learning framework for 3D medical image analysis. Med. Image Anal. 64, 101746 (2020).
Google Scholar
Chaitanya, K., Erdil, E., Karani, N. & Konukoglu, E. Contrastive learning of global and local features for medical image segmentation with limited annotations. Adv. Neural Inf. Process. Syst. 33, 12546–12558 (2020).
He, X. et al. Sample-efficient deep learning for COVID-19 diagnosis based on CT scans. Adv. Neural Inf. Process. Syst. 33, 12546–12558 (2020).
Li, H. et al. Imbalance-aware self-supervised learning for 3D radiomic representations. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds de Bruijne, M. et al.) 36–46 (Springer, 2021).
Liu, J. et al. Align, attend and locate: chest X-ray diagnosis via contrast induced attention network with limited supervision. In Proc. IEEE/CVF International Conference on Computer Vision 106321–10640 (IEEE, 2019).
Zhou, H.-Y. et al. Comparing to learn: surpassing ImageNet pretraining on radiographs by comparing image representations. In International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Martel, A. L.) 398–407 (Springer, 2020).
Soni, P. N., Shi, S., Sriram, P. R., Ng, A. Y. & Rajpurkar, P. Contrastive learning of heart and lung sounds for label-efficient diagnosis. Patterns 3, 100400 (2021).
Google Scholar
Liu, Q., Yu, L., Luo, L., Dou, Q. & Heng, P. A. Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Trans. Med. Imaging 39, 3429–3440 (2020).
Google Scholar
Wang, D., Zhang, Y., Zhang, K. & Wang, L. FocalMix: semi-supervised learning for 3D medical image detection. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 3950–3959 (IEEE, 2020).
Zhang, Y., Jiang, H., Miura, Y., Manning, C. D. & Langlotz, C. P. Contrastive learning of medical visual representations from paired images and text. In Proc. 7th Machine Learning for Healthcare Conference (eds Lipton, Z. et al.) 2–25 (PMLR, 2020).
Truong, T., Mohammadi, S. & Lenga, M. How transferable are self-supervised features in medical image classification tasks? In Proc. Machine Learning for Health (eds Roy, S. et al.) 54–74 (PMLR, 2021).
