Abstract
Image segmentation in the medical domain has gained a lot of research interest in recent years with the advancements in deep learning algorithms and related technologies. Medical image datasets are often imbalanced and to handle the imbalance problem, deep learning models are equipped with modified loss functions to effectively penalize the training weights for false predictions and conduct unbiased learning. Recent works have introduced various loss functions suitable for certain scenarios of segmentation. In this paper, we have explored the existing loss functions that are widely used for medical image segmentation, following which an accelerated Tversky loss (ATL) function is proposed that uses log cosh function to better optimize the gradients. The no-new U-Net (nn-Unet) model is adopted as the base model to validate the behaviour of the loss functions by using the standard benchmark segmentation performance metrics. To establish the robustness and effectiveness of the loss functions, multiple datasets are adopted, where ATL function illustrated better performance with faster convergence and better mask generation.
All authors have contributed equally.
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References
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Machine Intell. 39(12), 2481–2495 (2017)
Brosch, T., Yoo, Y., Tang, L.Y.W., Li, D.K.B., Traboulsee, A., Tam, R.: Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 3–11. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_1
Caliva, F., Iriondo, C., Martinez, A.M., Majumdar, S., Pedoia, V.: Distance map loss penalty term for semantic segmentation. arXiv preprint arXiv:1908.03679 (2019)
Zhang, Z., Wu, C., Coleman, S., Kerr, D.: Dense-inception U-Net for medical image segmentation. Comput. Methods Programs Biomed. 192, 105395 (2020). ISSN 0169-2607
Esteva, A., et al.: A guide to deep learning in healthcare. Nature Med. 25(1), 24–29 (2019)
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)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks (2018)
Isensee, F., et al.: Automated design of deep learning methods for biomedical image segmentation (2020). arXiv: 1904.08128 [cs.CV]
Jadon, S.: A survey of loss functions for semantic segmentation. arXiv preprint arXiv:2006.14822 (2020)
Jain, A., Ratnoo, S., Kumar., D.: Addressing class imbalance problem in medical diagnosis: a genetic algorithm approach. In: International Conference on Information, Communication, Instrumentation and Control, pp. 1–8 (2017)
Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., Ben Ayed, I.: Boundary loss for highly unbalanced segmentation. Medical Image Anal. 67, 101851 (2021). ISSN 1361–8415
Lei, T., Wang, R., Wan, Y., Du, X., Meng, H., Nandi, A.K.: Medical image segmentation using deep learning: a survey. arXiv preprint arXiv:2009.13120 (2020)
Lin, T., et al.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision, pp. 2999–3007 (2017)
Sankaran, P., et al.: Multi-task learning and weighted cross-entropy for DNN-based keyword spotting. In: Interspeech, vol. 9, pp. 760–764 (2016)
Punn, N.S., Agarwal, S.: Inception u-net architecture for semantic segmentation to identify nuclei in microscopy cell images. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 16(1), 1–15 (2020)
Punn, N.S., Agarwal. S.: Multi-modality encoded fusion with 3d inception U-Net and decoder model for brain tumor segmentation. In: Multimedia Tools and Applications, pp. 1–16 (2020)
Rahman, M.A., Wang, Y.: Optimizing intersection-over-union in deep neural networks for image segmentation. In: ISVC (2016)
Ribera, J., Güera, D., Chen, Y., Delp, E.: Weighted hausdorff distance: a loss function for object localization. ArXiv, abs/1806.07564 (2018)
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
Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms (2019). arXiv: 1902.09063 [cs.CV]
Suzuki, K.: Overview of deep learning in medical imaging. Radiol. Phys. Technol. 10(3), 257–273 (2017)
Szegedy C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Taghanaki, S.A., et al.: Combo loss: Handling input and output imbalance in multi-organ segmentation (2018). arXiv: 1805.02798 [cs.CV]
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J., Wu, Z., Ding, X.: Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Medical Image Anal. 63, 101693 (2020)
Wong, K.C.L., Moradi, M., Tang, H., Syeda-Mahmood, T.: 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) 3d segmentation with exponential logarithmic loss for highly unbalanced object sizes. LNCS, vol. 11072, pp. 612–619. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_70
Wu, Z., Shen, C., van den Hengel, A.: Bridging category-level and instance-level semantic image segmentation (2016). arXiv: 1605.06885 [cs.CV]
Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. Adv. Neural. Inf. Process. Syst. 31, 8778–8788 (2018)
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Nasalwai, N., Punn, N.S., Sonbhadra, S.K., Agarwal, S. (2021). Addressing the Class Imbalance Problem in Medical Image Segmentation via Accelerated Tversky Loss Function. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12714. Springer, Cham. https://doi.org/10.1007/978-3-030-75768-7_31
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