Abstract
When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted images are generated by injecting nine types of common corruptions into validation images. Besides, two classification and one ranking metrics are designed to evaluate the prediction and confidence performance under corruption. Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation. Our codes are available on https://github.com/superjamessyx/robustness_benchmark.
Y. Zhang and Y. Sun—Equal contribution.
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References
Azulay, A., Weiss, Y.: Why do deep convolutional networks generalize so poorly to small image transformations? arXiv preprint arXiv:1805.12177 (2018)
Bai, Y., Mei, J., Yuille, A.L., Xie, C.: Are transformers more robust than CNNs? Adv. Neural Inf. Process. Syst. 34 (2021)
Barisoni, L., Lafata, K.J., Hewitt, S.M., Madabhushi, A., Balis, U.G.: Digital pathology and computational image analysis in nephropathology. Nat. Rev. Nephrol. 16(11), 669–685 (2020)
Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 (2017)
Chen, M., Wang, Z., Zheng, F.: Benchmarks for corruption invariant person re-identification. arXiv preprint arXiv:2111.00880 (2021)
Clarke, E.L., Treanor, D.: Colour in digital pathology: a review. Histopathology 70(2), 153–163 (2017)
Dodge, S., Karam, L.: Understanding how image quality affects deep neural networks. In: 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–6. IEEE (2016)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM J. Discrete Math. 17(1), 134–160 (2003)
Farahani, N., Parwani, A.V., Pantanowitz, L., et al.: Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol. Lab. Med. Int. 7(23–33), 4321 (2015)
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231 (2018)
Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321–1330. PMLR (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261 (2019)
Jiang, X., Osl, M., Kim, J., Ohno-Machado, L.: Calibrating predictive model estimates to support personalized medicine. J. Am. Med. Inf. Assoc. 19(2), 263–274 (2012)
Kamann, C., Rother, C.: Benchmarking the robustness of semantic segmentation models with respect to common corruptions. Int. J. Comput. Vis. 129(2), 462–483 (2021)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012)
Liu, F., Hernandez-Cabronero, M., Sanchez, V., Marcellin, M.W., Bilgin, A.: The current role of image compression standards in medical imaging. Information 8(4), 131 (2017)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Michaelis, C., et al.: Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484 (2019)
Rohde, G.K., Ozolek, J.A., Parwani, A.V., Pantanowitz, L.: Carnegie mellon university bioimaging day 2014: challenges and opportunities in digital pathology. J. Pathol. Inf. 5 (2014)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Taqi, S.A., Sami, S.A., Sami, L.B., Zaki, S.A.: A review of artifacts in histopathology. J. Oral Maxillof. Pathol. 22(2), 279 (2018)
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347–10357. PMLR (2021)
Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant CNNs for digital pathology. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 210–218. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_24
Wang, J., Jin, S., Liu, W., Liu, W., Qian, C., Luo, P.: When human pose estimation meets robustness: adversarial algorithms and benchmarks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11855–11864 (2021)
Wang, N.C., Kaplan, J., Lee, J., Hodgin, J., Udager, A., Rao, A.: Stress testing pathology models with generated artifacts. J. Pathol. Inf. 12 (2021)
Yamashita, R., Long, J., Banda, S., Shen, J., Rubin, D.L.: Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation. IEEE Trans. Med. Imaging 40(12), 3945–3954 (2021)
Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
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This work was funded by China Postdoctoral Science Foundation (2021M702922).
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Zhang, Y., Sun, Y., Li, H., Zheng, S., Zhu, C., Yang, L. (2022). Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_24
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