Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR

van der Walt, S., Schönberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T., the scikit-image contributors: scikit-image: image processing in Python. PeerJ 2

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2015

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Christ, P.F., Elshaer, M.E.A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., Rempfler, M., Armbruster, M., Hofmann, F., D’Anastasi, M., Sommer, W.H., Ahmadi, S.A., Menze, B.H.: Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields. In: Med. Image Comput. Comput. Interv. – MICCAI 2016. pp. 415–423. Springer International Publishing (2016). https://doi.org/10.1007/978-3-319-46723-8_48

2016

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Hu, P., Wu, F., Peng, J., Liang, P., Kong, D.: Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys. Med. Biol. 61

2016

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Christ, P.F., Ettlinger, F., Grün, F., Elshaera, M.E.A., Lipkova, J., Schlecht, S., Ahmaddy, F., Tatavarty, S., Bickel, M., Bilic, P., Rempfler, M., Hofmann, F., Anastasi, M.D., Ahmadi, S.A., Kaissis, G., Holch, J., Sommer, W., Braren, R., Heinemann, V., Menze, B.: Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks (2017)

2017

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Yuan, Y.: Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation (2017)

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Asrani, S.K., Devarbhavi, H., Eaton, J., Kamath, P.S.: Burden of liver diseases in the world. J. Hepatol. 70

2018

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Vorontsov, E., Tang, A., Pal, C., Kadoury, S.: Liver lesion segmentation informed by joint liver segmentation. In: 2018 IEEE 15th Int. Symp. Biomed. Imaging (ISBI 2018). pp. 1332–1335. IEEE (2018). https://doi.org/10.1109/ISBI.2018.8363817

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Ouhmich, F., Agnus, V., Noblet, V., Heitz, F., Pessaux, P.: Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks. Int. J. Comput. Assist. Radiol. Surg. 14

2019

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PyTorch: OneCycleLR — PyTorch 1.9.0 documentation, https://pytorch.org/docs/stable/generated/torch.optim.lr\_scheduler.OneCycleLR.html\#torch.optim.lr\_scheduler.OneCycleLR

Perslev, M., Dam, E.B., Pai, A., Igel, C.: One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation. In: Med. Image Comput. Comput. Assist. Interv. – MICCAI 2019. pp. 30–38 (2019). https://doi.org/10.1007/978-3-030-32245-8_4

2019

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Smith, L.N., Topin, N.: Super-convergence: very fast training of neural networks using large learning rates. In: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. vol. 11006. SPIE-Intl Soc Optical Eng (may 2019). https://doi.org/10.1117/12.2520589, https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11006/1100612/Super-convergence--very-fast-training-of-neural-networks-using/10.1117/12.2520589.short

2019

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Tian, J., Liu, L., Shi, Z., Xu, F.: Automatic Couinaud Segmentation from CT Volumes on Liver Using GLC-UNet. In: Med. Image Comput. Comput. Assist. Interv. – MICCAI 2019. pp. 274–282. Springer International Publishing (2019). https://doi.org/10.1007/978-3-030-32692-0_32

2019

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