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We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier.
Qiao, S., Wang, H., Liu, C., Shen, W., Yuille, A., 2019 · 1903
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Frodo: Free rejection of out-of-distribution samples: application to chest x-ray analysis
Çallı, E., Murphy, K., Sogancioglu, E., Van Ginneken, B., 2019 · 1907
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Deep ensembles: A loss landscape perspective
Fort, S., Hu, H., Lakshminarayanan, B., 2019 · 1912
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Big transfer (bit): General visual representation learning
Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., Houlsby, N., 2019 · 1912
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J., 2000 · 2000
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Pretrained transformers improve out-of-distribution robustness
Hendrycks, D., Liu, X., Wallace, E., Dziedzic, A., Krishnan, R., Song, D., 2020b · 2004
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Calibrating healthcare ai: Towards reliable and interpretable deep predictive models
Thiagarajan, J.J., Sattigeri, P., Rajan, D., Venkatesh, B., 2020 · 2004
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al., 2020a · 2006
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A benchmark of medical out of distribution detection
Cao, T., Huang, C., Hui, D.Y.T., Cohen, J.P., 2020 · 2007
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Contrastive training for improved out-of-distribution detection
Winkens, J., Bunel, R., Roy, A.G., Stanforth, R., Natarajan, V., Ledsam, J.R., MacWilliams, P., Kohli, P., Karthikesalingam, A., Kohl, S., et al., 2020 · 2007
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Zhou, S.K., Greenspan, H., Davatzikos, C., Duncan, J.S., van Ginneken, B., Madabhushi, A., Prince, J.L., Rueckert, D., Summers, R.M., 2020 · 2008
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Self-supervised out-of-distribution detection in brain ct scans
Venkatakrishnan, A.R., Kim, S.T., Eisawy, R., Pfister, F., Navab, N., 2020 · 2011
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Explaining and harnessing adversarial examples
Goodfellow, I.J., Shlens, J., Szegedy, C., 2014 · 2014
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Approval of artificial intelligence and machine learning-based medical devices in the usa and europe (2015–20): a comparative analysis
Muehlematter, U.J., Daniore, P., Vokinger, K.N., 2021 · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., Clune, J., 2015 · 2015
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HD-CNN: hierarchical deep convolutional neural networks for large scale visual recognition
Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., DeCoste, D., Di, W., Yu, Y., 2015 · 2015
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D., Gimpel, K., 2017 · 2017
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Ensembles of multiple models and architectures for robust brain tumour segmentation
Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Rueckert, D., et al., 2017 · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., Blundell, C., 2017 · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Sun, C., Shrivastava, A., Singh, S., Gupta, A., 2017 · 2017
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Universum prescription: Regularization using unlabeled data
Zhang, X., LeCun, Y., 2017 · 2017
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Uncertainty estimation in deep neural networks for dermoscopic image classification
Combalia, M., Hueto, F., Puig, S., Malvehy, J., Vilaplana, V., 2020 · 2020
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Out-of-distribution detection for skin lesion images with deep isolation forest
Li, X., Lu, Y., Desrosiers, C., Liu, X., 2020 · 2020
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Efficient out-of-distribution detection in digital pathology using multi-head convolutional neural networks
Linmans, J., van der Laak, J., Litjens, G., 2020 · 2020
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A deep learning system for differential diagnosis of skin diseases
Liu, Y., Jain, A., Eng, C., Way, D.H., Lee, K., Bui, P., Kanada, K., de Oliveira Marinho, G., Gallegos, J., Gabriele, S., et al., 2020 · 2020
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On out-of-distribution detection algorithms with deep neural skin cancer classifiers
Pacheco, A.G., Sastry, C.S., Trappenberg, T., Oore, S., Krohling, R.A., 2020 · 2020
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Lee, K., Lee, K., Lee, H., Shin, J., 2018 · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., Srikant, R., 2018 · 2018
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Prototypical clustering networks for dermatological disease diagnosis
Prabhu, V., Kannan, A., Ravuri, M., Chablani, M., Sontag, D., Amatriain, X., 2018 · 2018
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Group normalization
Wu, Y., He, K., 2018 · 2018
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Noise contrastive priors for functional uncertainty
Hafner, D., Tran, D., Lillicrap, T., Irpan, A., Davidson, J., 2019 · 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
Liu, X., Faes, L., Kale, A.U., Wagner, S.K., Fu, D.J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al., 2019 · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J.V., Lakshminarayanan, B., Snoek, J., 2019 · 2019
Cited alongside, same era.
Uncertainty estimation using a single deep deterministic neural network
Van Amersfoort, J., Smith, L., Teh, Y.W., Gal, Y., 2020 · 2020
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Addressing the real-world class imbalance problem in dermatology
Weng, W.H., Deaton, J., Natarajan, V., Elsayed, G.F., Liu, Y., 2020 · 2020
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Hyperparameter ensembles for robustness and uncertainty quantification
Wenzel, F., Snoek, J., Tran, D., Jenatton, R., 2020 · 2020
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Open set deep networks based on extreme value theory (EVT) for open set recognition in skin disease classification
Yasin, Y., Rumala, D.J., Purnomo, M.H., Ratna, A.A.P., Hidayati, A.N., Nurtanio, I., Rachmadi, R.F., Purnama, I.K.E., 2020 · 2020
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Hybrid models for open set recognition
Zhang, H., Li, A., Guo, J., Guo, Y., 2020 · 2020
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Big self-supervised models advance medical image classification
Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., et al., 2021 · 2021
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Density of states estimation for out of distribution detection
Morningstar, W., Ham, C., Gallagher, A., Lakshminarayanan, B., Alemi, A., Dillon, J., 2021 · 2021
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Supervised transfer learning at scale for medical imaging
Mustafa, B., Loh, A., Freyberg, J., MacWilliams, P., Karthikesalingam, A., Houlsby, N., Natarajan, V., 2021 · 2021
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Chexseen: Unseen disease detection for deep learning interpretation of chest x-rays
Shi, S., Malhi, I., Tran, K., Ng, A.Y., Rajpurkar, P., 2021 · 2021
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A simple and effective baseline for out-of-distribution detection using abstention URL: https://openreview.net/forum?id=q_Q9MMGwSQu
Thulasidasan, S., Thapa, S., Dhaubhadel, S., Chennupati, G., Bhattacharya, T., Bilmes, J., 2021 · 2021
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