Abstract
Near Out-of-Distribution (OOD) detection is a crucial issue in medical applications, as misdiagnosis caused by the presence of rare diseases inevitablely poses a significant risk. Recently, several deep learning-based methods for OOD detection with uncertainty estimation, such as the Evidential Deep Learning (EDL) and its variants, have shown remarkable performance in identifying outliers that significantly differ from training samples. Nevertheless, few studies focus on the great challenge of near OOD detection problem, which involves detecting outliers that are close to the training distribution, as commonly encountered in medical image application. To address this limitation and reduce the risk of misdiagnosis, we propose an Evidence Reconciled Neural Network (ERNN). Concretely, we reform the evidence representation obtained from the evidential head with the proposed Evidential Reconcile Block (ERB), which restricts the decision boundary of the model and further improves the performance in near OOD detection. Compared with the state-of-the-art uncertainty-based methods for OOD detection, our method reduces the evidential error and enhances the capability of near OOD detection in medical applications. The experiments on both the ISIC2019 dataset and an in-house pancreas tumor dataset validate the robustness and effectiveness of our approach. Code for ERNN has been released at https://github.com/KellaDoe/ERNN.
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References
Berger, C., Paschali, M., Glocker, B., Kamnitsas, K.: Confidence-Based Out-of-Distribution Detection: A Comparative Study and Analysis. In: Sudre, C.H., et al. (eds.) UNSURE/PIPPI -2021. LNCS, vol. 12959, pp. 122–132. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87735-4_12
Charpentier, B., Zügner, D., Günnemann, S.: Posterior network: uncertainty estimation without OOD samples via density-based pseudo-counts. Adv. Neural Inf. Process. Syst. 33, 1356–1367 (2020)
Codella, N.C., 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: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 168–172. IEEE (2018)
Combalia, M., et al.: Bcn20000: dermoscopic lesions in the wild. arXiv preprint arXiv:1908.02288 (2019)
Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: CVPR09 (2009)
Geng, C., Huang, S.j., Chen, S.: Recent advances in open set recognition: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3614–3631 (2020)
Ghafoorian, M., et al.: Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516–524. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_59
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)
Jøsang, A.: Subjective logic, vol. 4. Springer (2016). https://doi.org/10.1007/978-3-319-42337-1
Liu, W., Yue, X., Chen, Y., Denoeux, T.: Trusted multi-view deep learning with opinion aggregation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 7585–7593 (2022)
Malinin, A., Gales, M.: Predictive uncertainty estimation via prior networks. Adv. Neural Inf. Process. Syst. 31 (2018)
Malinin, A., Gales, M.: Reverse kl-divergence training of prior networks: improved uncertainty and adversarial robustness. Adv. Neural Information Process. Syst. 32 (2019)
Mehta, D., Gal, Y., Bowling, A., Bonnington, P., Ge, Z.: Out-of-distribution detection for long-tailed and fine-grained skin lesion images. In: Medical Image Computing and Computer Assisted Intervention-MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part I. pp. 732–742. Springer (2022). https://doi.org/10.1007/978-3-031-16431-6_69
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
Roy, A.G., et al.: Does your dermatology classifier know what it doesn’t know? detecting the long-tail of unseen conditions. Med. Image Anal. 75, 102274 (2022)
Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. Adv. Neural Information Process. Syst. 31 (2018)
Thulasidasan, S., Chennupati, G., Bilmes, J.A., Bhattacharya, T., Michalak, S.: On mixup training: improved calibration and predictive uncertainty for deep neural networks. Adv. Neural Information Process. Syst. 32 (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(1), 1–9 (2018)
Ulmer, D., Meijerink, L., Cinà, G.: Trust issues: uncertainty estimation does not enable reliable OOD detection on medical tabular data. In: Machine Learning for Health. pp. 341–354. PMLR (2020)
Winkens, J., et al.: Contrastive training for improved out-of-distribution detection. arXiv preprint arXiv:2007.05566 (2020)
Yang, H.M., Zhang, X.Y., Yin, F., Liu, C.L.: Robust classification with convolutional prototype learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3474–3482 (2018)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
Zhao, X., Ou, Y., Kaplan, L., Chen, F., Cho, J.H.: Quantifying classification uncertainty using regularized evidential neural networks. arXiv preprint arXiv:1910.06864 (2019)
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This work was supported by the National Natural Science Foundation of China (No. 62173252, 61976134), and Natural Science Foundation of Shanghai (No. 21ZR1423900).
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Fu, W., Chen, Y., Liu, W., Yue, X., Ma, C. (2023). Evidence Reconciled Neural Network for Out-of-Distribution Detection in Medical Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_30
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