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Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection.
Using imaging biomarkers to accelerate drug development and clinical trials
Pien, H. H., Fischman, A. J., Thrall, J. H., and Sorensen, A. G · 2005
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Visualizing data using t-sne
Maaten, L. v. d. and Hinton, G · 2008
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Mitosis detection in breast cancer histology images with deep neural networks
Ciresan, D. C., Giusti, A., Gambardella, L. M., and Schmidhuber, J · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Early/ diagnosis of alzheimer’s disease with deep learning
Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Deep similarity learning for multimodal medical images
Cheng, X., Zhang, L., and Zhang, L · 2015
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Learning to boost filamentary structure segmentation
Gu, L. and Cheng, L · 2015
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Kaggle diabetic retinopathy detection challenge
Kaggle · 2015
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The multimodal brain tumor image segmentation benchmark (brats)
Menze, B. H., Jakab, A., Bauer, S., and et al · 2015
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Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation
Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., and Summers, R. M · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S · 2017
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Detecting adversarial samples from artifacts
Feinman, R., Curtin, R. R., Shintre, S., and Gardner, A. B · 2017
Cited alongside, same era.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I. J., and Bengio, S · 2017
Cited alongside, same era.
Safetynet: Detecting and rejecting adversarial examples robustly
Lu, J., Issaranon, T., and Forsyth, D. A · 2017
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On detecting adversarial perturbations
Metzen, J. H., Genewein, T., Fischer, V., and Bischoff, B · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Cited alongside, same era.
The space of transferable adversarial examples
Tramèr, F., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2017
Cited alongside, same era.
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Ross, A. S. and Doshi-Velez, F · 2018
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Hilbert-based generative defense for adversarial examples
Bai, Y., Feng, Y., Wang, Y., Dai, T., Xia, S.-T., and Jiang, Y · 2019
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Adversarial attacks on medical machine learning
Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L., and Kohane, I. S · 2019
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The international skin imaging collaboration
ISIC · 2019
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Black-box adversarial attacks on video recognition models
Jiang, L., Ma, X., Chen, S., Bailey, J., and Jiang, Y.-G · 2019
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Volumetric medical image segmentation: A 3d deep coarse-to-fine framework and its adversarial examples
Li, Y., Zhu, Z., Zhou, Y., Xia, Y., Shen, W., Fishman, E. K., and Yuille, A. L · 2019
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D. A · 2018
Cited alongside, same era.
Adaptive hash retrieval with kernel based similarity
Bai, X., Yan, C., Yang, H., Bai, L., Zhou, J., and Hancock, E. R · 2018
Cited alongside, same era.
Appearance-based gaze estimation via evaluation-guided asymmetric regression
Cheng, Y., Lu, F., and Zhang, X · 2018
Cited alongside, same era.
Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2018
Cited alongside, same era.
Symps: BRDF symmetry guided photometric stereo for shape and light source estimation
Lu, F., Chen, X., Sato, I., and Sato, Y · 2018
Cited alongside, same era.
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Unsupervised ensemble strategy for retinal vessel segmentation
Liu, B., Gu, L., and Lu, F · 2019
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Pathological evidence exploration in deep retinal image diagnosis
Niu, Y., Gu, L., Lu, F., Lv, F., Wang, Z., Sato, I., Zhang, Z., Xiao, Y., Dai, X., and Cheng, T · 2019
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Adversarial training for free!
Shafahi, A., Najibi, M., Ghiasi, M. A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., and Goldstein, T · 2019
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Multiscale visual attention networks for object detection in vhr remote sensing images
Wang, C., Bai, X., Wang, S., Zhou, J., and Ren, P · 2019
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Towards noise-robust neural networks via progressive adversarial training
Yu, H., Liu, A., Liu, X., Yang, J., and Zhang, C · 2019
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Interpreting and improving adversarial robustness with neuron sensitivity
Zhang, C., Liu, A., Liu, X., Xu, Y., Yu, H., Ma, Y., and Li, T · 2019
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Improving adversarial robustness requires revisiting misclassified examples
Wang, Y., Zou, D., Yi, J., Bailey, J., Ma, X., and Gu, Q · 2020
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Skip connections matter: On the transferability of adversarial examples generated with resnets
Wu, D., Wang, Y., Xia, S.-T., Bailey, J., and Ma, X · 2020
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Learning binary code for fast nearest subspace search
Zhou, L., Bai, X., Liu, X., Zhou, J., and Hancock, E. R · 2020
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