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A ubiquitous challenge in machine learning is the problem of domain generalisation.
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Improving predictive inference under covariate shift by weighting the log-likelihood function
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Dataset shift in machine learning
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Imagenet: A large-scale hierarchical image database
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Principal component analysis
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A theory of learning from different domains
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A kernel two-sample test
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Domain generalization via invariant feature representation
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Generative adversarial nets
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The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions
R. J. Hay, N. E. Johns, H. C. Williams, I. W. Bolliger, R. P. Dellavalle, D. J. Margolis, R. Marks, L. Naldi, M. A. Weinstock, S. K. Wulf, C. Michaud, C. J. L. Murray, and M. Naghavi · 2014
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Dermatologist-level classification of skin cancer with deep neural networks
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The burden of skin disease in the United States
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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CheXNet: Radiologist-level pneumonia detection on chest x-rays with deep learning
P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya, et al · 2017
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Radiologist shortage leaves patient care at risk, warns royal college
A. Rimmer · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
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ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers · 2017
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
P. Bandi, O. Geessink, Q. Manson, M. Van Dijk, M. Balkenhol, M. Hermsen, B. E. Bejnordi, B. Lee, K. Paeng, A. Zhong, et al · 2018
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Generating highly realistic images of skin lesions with gans
C. Baur, S. Albarqouni, and N. Navab · 2018
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Clinically applicable deep learning for diagnosis and referral in retinal disease
J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. Hughes, R. Raine, J. Hughes, D. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. Khaw, M. Suleyman, J. Corebise, P. Keane, and O. Ronneberger · 2018
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GAN-based synthetic medical image augmentation for increased cnn performance in liver lesion classification
M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan · 2018
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Potential biases in machine learning algorithms using electronic health record data
M. A. Gianfrancesco, S. Tamang, J. Yazdany, and G. Schmajuk · 2018
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Domain generalization with adversarial feature learning
H. Li, S. J. Pan, S. Wang, and A. C. Kot · 2018
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Ensuring fairness in machine learning to advance health equity
A. Rajkomar, M. Hardt, M. D. Howell, G. Corrado, and M. H. Chin · 2018
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Classify epithelium-stroma in histopathological images based on deep transferable network
X. Yu, H. Zheng, C. Liu, Y. Huang, and X. Ding · 2018
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Synthesizing retinal and neuronal images with generative adversarial nets
H. Zhao, H. Li, S. Maurer-Stroh, and L. Cheng · 2018
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Generalizing to unseen domains via distribution matching
I. Albuquerque, J. Monteiro, M. Darvishi, T. H. Falk, and I. Mitliagkas · 2019
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End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
D. Ardila, A. P. Kiraly, S. Bharadwaj, B. Choi, J. J. Reicher, L. Peng, D. Tse, M. Etemadi, W. Ye, G. Corrado, D. Naidich, and S. Shetty · 2019
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CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison
J. Irvin, P. Rajpurkar, M. Ko, Y. Yu, S. Ciurea-Ilcus, C. Chute, H. Marklund, B. Haghgoo, R. Ball, K. Shpanskaya, et al · 2019
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Signed laplacian deep learning with adversarial augmentation for improved mammography diagnosis
H. Li, D. Chen, W. H. Nailon, M. E. Davies, and D. I. Laurenson · 2019
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A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
X. Liu, L. Faes, A. U. Kale, S. K. Wagner, D. J. Fu, A. Bruynseels, T. Mahendiran, G. Moraes, M. Shamdas, C. Kern, et al · 2019
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Skin lesion classification using gan based data augmentation
Development and assessment of an artificial intelligence–based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices
A. Jain, D. Way, V. Gupta, Y. Gao, G. de Oliveira Marinho, J. Hartford, R. Sayres, K. Kanada, C. Eng, K. Nagpal, K. B. Desalvo, G. S. Corrado, L. Peng, D. R. Webster, R. C. Dunn, D. Coz, S. J. Huang, Y. Liu, P. Bui, and Y. Liu · 2021
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Leveraging regular fundus images for training uwf fundus diagnosis models via adversarial learning and pseudo-labeling
L. Ju, X. Wang, X. Zhao, P. Bonnington, T. Drummond, and Z. Ge · 2021
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End-to-end privacy preserving deep learning on multi-institutional medical imaging
G. Kaissis, A. Ziller, J. Passerat-Palmbach, T. Ryffel, D. Usynin, A. Trask, I. Lima, J. Mancuso, F. Jungmann, M.-M. Steinborn, et al · 2021
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A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability
S. M. Khan, X. Liu, S. Nath, E. Korot, L. Faes, S. K. Wagner, P. A. Keane, N. J. Sebire, M. J. Burton, and A. K. Denniston · 2021
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H. Rashid, M. A. Tanveer, and H. A. Khan · 2019
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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
D. Tellez, G. Litjens, P. Bándi, W. Bulten, J.-M. Bokhorst, F. Ciompi, and J. Van Der Laak · 2019
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Risk of training diagnostic algorithms on data with demographic bias
S. Abbasi-Sureshjani, R. Raumanns, B. E. Michels, G. Schouten, and V. Cheplygina · 2020
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Causality matters in medical imaging
D. C. Castro, I. Walker, and B. Glocker · 2020
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RandAugment: Practical automated data augmentation with a reduced search space
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2020
Cited alongside, same era.
Achieving robustness in the wild via adversarial mixing with disentangled representations
S. Gowal, C. Qin, P.-S. Huang, T. Cemgil, K. Dvijotham, T. Mann, and P. Kohli · 2020
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In search of lost domain generalization
I. Gulrajani and D. Lopez-Paz · 2020
Cited alongside, same era.
Breaking medical data sharing boundaries by using synthesized radiographs
T. Han, S. Nebelung, C. Haarburger, N. Horst, S. Reinartz, D. Merhof, F. Kiessling, V. Schulz, and D. Truhn · 2020
Cited alongside, same era.
A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen · 2021
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Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
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Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
L. Seyyed-Kalantari, H. Zhang, M. McDermott, I. Y. Chen, and M. Ghassemi · 2021
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Fairness for unobserved characteristics: Insights from technological impacts on queer communities
N. Tomasev, K. R. McKee, J. Kay, and S. Mohamed · 2021
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A fine-grained analysis on distribution shift
O. Wiles, S. Gowal, F. Stimberg, S.-A. Rebuffi, I. Ktena, K. D. Dvijotham, and A. T. Cemgil · 2021
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https://www.cancer.org/cancer/melanoma-skin-cancer/about/key-statistics.html , 2022
Key statistics for melanoma skin cancer · 2022
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Robust and efficient medical imaging with self-supervision
S. Azizi, L. Culp, J. Freyberg, B. Mustafa, S. Baur, S. Kornblith, T. Chen, P. MacWilliams, S. S. Mahdavi, E. Wulczyn, et al · 2022
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Detecting and preventing shortcut learning for fair medical ai using shortcut testing (short)
A. Brown, N. Tomasev, J. Freyberg, Y. Liu, A. Karthikesalingam, and J. Schrouff · 2022
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A clarification of the nuances in the fairness metrics landscape
A. Castelnovo, R. Crupi, G. Greco, D. Regoli, I. G. Penco, and A. C. Cosentini · 2022
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RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
P. Chambon, C. Bluethgen, J.-B. Delbrouck, R. Van der Sluijs, M. Połacin, J. M. Z. Chaves, T. M. Abraham, S. Purohit, C. P. Langlotz, and A. Chaudhari · 2022
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Generative adversarial networks in medical image augmentation: a review
Y. Chen, X.-H. Yang, Z. Wei, A. A. Heidari, N. Zheng, Z. Li, H. Chen, H. Hu, Q. Zhou, and Q. Guan · 2022
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Classifier-free diffusion guidance
J. Ho and T. Salimans · 2022
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Cascaded diffusion models for high fidelity image generation
J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans · 2022
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Medical domain knowledge in domain-agnostic generative AI
J. N. Kather, N. Ghaffari Laleh, S. Foersch, and D. Truhn · 2022
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Medical Diffusion–Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
F. Khader, G. Mueller-Franzes, S. T. Arasteh, T. Han, C. Haarburger, M. Schulze-Hagen, P. Schad, S. Engelhardt, B. Baessler, S. Foersch, et al · 2022
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Fairness in cardiac magnetic resonance imaging: assessing sex and racial bias in deep learning-based segmentation
E. Puyol-Antón, B. Ruijsink, J. Mariscal Harana, S. K. Piechnik, S. Neubauer, S. E. Petersen, R. Razavi, P. Chowienczyk, and A. P. King · 2022
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Addressing fairness in artificial intelligence for medical imaging
M. A. Ricci Lara, R. Echeveste, and E. Ferrante · 2022
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J. Schrouff, N. Harris, O. Koyejo, I. Alabdulmohsin, E. Schnider, K. Opsahl-Ong, A. Brown, S. Roy, D. Mincu, C. Chen, A. Dieng, Y. Liu, V. Natarajan, A. Karthikesalingam, K. Heller, S. Chiappa, and A. D’amour · 2022
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Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
G. Somepalli, V. Singla, M. Goldblum, J. Geiping, and T. Goldstein · 2022
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Machine learning for medical imaging: methodological failures and recommendations for the future
G. Varoquaux and V. Cheplygina · 2022
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Shifting machine learning for healthcare from development to deployment and from models to data
A. Zhang, L. Xing, J. Zou, and J. C. Wu · 2022
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Extracting training data from diffusion models
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramèr, B. Balle, D. Ippolito, and E. Wallace · 2023
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