Abstract
Automated image analysis of skin lesions has potential to improve diagnostic decision making. A clinically useful system should be selective, rejecting images it is ill-equipped to classify, for example because they are of lesion types not represented well in training data. Furthermore, lesion classifiers should support cost-sensitive decision making. We investigate methods for selective, cost-sensitive classification of lesions as benign or malignant using test images of lesion types represented and not represented in training data. We propose EC-SelectiveNet, a modification to SelectiveNet that discards the selection head at test time, making decisions based on expected costs instead. Experiments show that training for full coverage is beneficial even when operating at lower coverage, and that EC-SelectiveNet outperforms standard cross-entropy training, whether or not temperature scaling or Monte Carlo dropout averaging are used, in both symmetric and asymmetric cost settings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
GitHub Repository: https://github.com/UoD-CVIP/Selective_Dermatology.
References
Brinker, T.J., et al.: Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur. J. Cancer 113, 47–54 (2019)
Carse, J., McKenna, S.: Active learning for patch-based digital pathology using convolutional neural networks to reduce annotation costs. In: Reyes-Aldasoro, C.C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds.) ECDP 2019. LNCS, vol. 11435, pp. 20–27. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23937-4_3
Codella, N., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: IEEE ISBI, pp. 168–172 (2018)
Combalia, M., et al: BCN20000: dermoscopic lesions in the wild. arXiv preprint arXiv:1908.02288 (2019)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255 (2009)
DeVries, T., Taylor, G.W.: Leveraging uncertainty estimates for predicting segmentation quality. In: Conference on Medical Imaging with Deep Learning (MIDL) (2018)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–8 (2017)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on Machine Learning (ICML), vol. PMLR 48, pp. 1050–1059 (2016)
Geifman, Y., El-Yaniv, R.: SelectiveNet: a deep neural network with an integrated reject option. In: Proceedings of the 36th International Conference on Machine Learning (ICML), vol. PMLR 97, pp. 2151–2159 (2019)
Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning (ICML), vol. PMLR 70, pp. 1321–1330 (2017)
Haenssle, H.A., Fink, C., Schneiderbauer, R., et al.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29(8), 1836–1842 (2018)
Han, S.S., Kim, M.S., Lim, W., Park, G.H., Park, I., Chang, S.E.: Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J. Inv. Dermatol. 138(7), 1529–1538 (2018)
Han, S.S., et al.: Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders. J. Inv. Dermatol. 140(9), 1753–1761 (2020)
Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: ICLR (2017)
Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580 (2012)
Kawahara, J., Hamarneh, G.: Visual diagnosis of dermatological disorders: human and machine performance. arxiv:1906.01256, 6 (2019)
Mårtensson, G., et al.: The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study. Med. Image Anal. 66, 101714 (2020)
Mobiny, A., Singh, A., Van Nguyen, H.: Risk-aware machine learning classifier for skin lesion diagnosis. J. Clin. Med. 8(8), 1241 (2019)
Mozafari, A.S., Gomes, H.S., Leão, W., Janny, S., Gagné, C.: Attended temperature scaling: a practical approach for calibrating deep neural networks. arXiv preprint arXiv:1810.11586 (2018)
Nixon, J., Dusenberry, M.W., Zhang, L., Jerfel, G., Tran, D.: Measuring calibration in deep learning. In: CVPR Workshops, vol. 2 (2019)
Smith, L.: Cyclical learning rates for training neural networks. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472. IEEE (2017)
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML), vol. PMLR 97, pp. 6105–6114 (2019)
Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)
Acknowledgments
This paper reports independent research funded by the National Institute for Health Research (Artificial Intelligence, Deep learning for effective triaging of skin disease in the NHS, AI_AWARD01901) and NHSX. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health Research, NHSX or the Department of Health and Social Care. This research was also funded by the Detect Cancer Early programme, and the Discovery Institute of Dermatology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Carse, J. et al. (2021). Robust Selective Classification of Skin Lesions with Asymmetric Costs. In: Sudre, C.H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. UNSURE PIPPI 2021 2021. Lecture Notes in Computer Science(), vol 12959. Springer, Cham. https://doi.org/10.1007/978-3-030-87735-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-87735-4_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87734-7
Online ISBN: 978-3-030-87735-4
eBook Packages: Computer ScienceComputer Science (R0)
