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Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year.
The liver tumor segmentation benchmark (lits)
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Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., Sutskever, I., 2019 · 1904
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Tumor burden analysis on computed tomography by automated liver and tumor segmentation
Linguraru, M.G., Richbourg, W.J., Liu, J., Watt, J.M., Pamulapati, V., Wang, S., Summers, R.M., 2012 · 1976
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Estimates of worldwide burden of cancer in 2008: Globocan 2008
Ferlay, J., Shin, H.R., Bray, F., Forman, D., Mathers, C., Parkin, D.M., 2010 · 2008
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Imagenet: A large-scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition, Ieee. pp. 248–255
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
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A likelihood and local constraint level set model for liver tumor segmentation from ct volumes
Li, C., Wang, X., Eberl, S., Fulham, M., Yin, Y., Chen, J., Feng, D.D., 2013 · 2013
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Automatic liver segmentation based on shape constraints and deformable graph cut in ct images
Li, G., Chen, X., Shi, F., Zhu, W., Tian, J., Xiang, D., 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the global burden of disease study 2015
Wang, H., Naghavi, M., Allen, C., Barber, R.M., Bhutta, Z.A., Carter, A., Casey, D.C., Charlson, F.J., Chen, A.Z., Coates, M.M., et al., 2016 · 2015
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Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
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Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation, in: Reconstruction, segmentation, and analysis of medical images. Springer, pp. 83–94
Poudel, R.P., Lamata, P., Montana, G., 2016 · 2016
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Rethinking atrous convolution for semantic image segmentation
Chen, L.C., Papandreou, G., Schroff, F., Adam, H., 2017 · 2017
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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., Ahmadi, S.A., Kaissis, G., Holch, J., Sommer, W., Braren, R., Heinemann, V., Menze, B., 2017 · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., 2017 · 2017
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Pyramid scene parsing network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881–2890
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J., 2017 · 2017
Cited alongside, same era.
Automatic liver tumor segmentation in ct with fully convolutional neural networks and object-based postprocessing
Chlebus, G., Schenk, A., Moltz, J.H., van Ginneken, B., Hahn, H.K., Meine, H., 2018 · 2018
Cited alongside, same era.
Attention u-net: Learning where to look for the pancreas
Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., Glocker, B., Rueckert, D., 2018 · 2018
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Image transformer, in: International conference on machine learning, PMLR. pp. 4055–4064
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., Tran, D., 2018 · 2018
Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation
Rezaei, M., Yang, H., Meinel, C., 2020 · 2020
Later among the works it cites.
An automatic method for segmentation of liver lesions in computed tomography images using deep neural networks
Araújo, J.D.L., da Cruz, L.B., Ferreira, J.L., da Silva Neto, O.P., Silva, A.C., de Paiva, A.C., Gattass, M., 2021 · 2021
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Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced mri using deep learning
Bousabarah, K., Letzen, B., Tefera, J., Savic, L., Schobert, I., Schlachter, T., Staib, L.H., Kocher, M., Chapiro, J., Lin, M., 2021 · 2021
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Swin-unet: Unet-like pure transformer for medical image segmentation
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M., 2021 · 2021
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Transunet: Transformers make strong encoders for medical image segmentation
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Cited alongside, same era.
Liver lesion segmentation informed by joint liver segmentation, in: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE. pp. 1332–1335
Vorontsov, E., Tang, A., Pal, C., Kadoury, S., 2018 · 2018
Cited alongside, same era.
Non-local neural networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7794–7803
Wang, X., Girshick, R., Gupta, A., He, K., 2018 · 2018
Cited alongside, same era.
Modified u-net (mu-net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in ct images
Seo, H., Huang, C., Bassenne, M., Xiao, R., Xing, L., 2019 · 2019
Cited alongside, same era.
Deep learning for automated segmentation of liver lesions at ct in patients with colorectal cancer liver metastases
Vorontsov, E., Cerny, M., Régnier, P., Di Jorio, L., Pal, C.J., Lapointe, R., Vandenbroucke-Menu, F., Turcotte, S., Kadoury, S., Tang, A., 2019 · 2019
Cited alongside, same era.
Deep attentive features for prostate segmentation in 3d transrectal ultrasound
Wang, Y., Dou, H., Hu, X., Zhu, L., Yang, X., Xu, M., Qin, J., Heng, P.A., Wang, T., Ni, D., 2019 · 2019
Cited alongside, same era.
Dense-unet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network
Cai, S., Tian, Y., Lui, H., Zeng, H., Wu, Y., Chen, G., 2020 · 2020
Cited alongside, same era.
Ma-net: A multi-scale attention network for liver and tumor segmentation
Fan, T., Wang, G., Li, Y., Wang, H., 2020 · 2020
Cited alongside, same era.
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y., 2021 · 2021
Later among the works it cites.
X-net: Multi-branch unet-like network for liver and tumor segmentation from 3d abdominal ct scans
Chi, J., Han, X., Wu, C., Wang, H., Ji, P., 2021 · 2021
Later among the works it cites.
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H., 2021 · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B., 2021 · 2021
Later among the works it cites.
Training data-efficient image transformers & distillation through attention, in: International Conference on Machine Learning, PMLR. pp. 10347–10357
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H., 2021 · 2021
Later among the works it cites.
Medical transformer: Gated axial-attention for medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 36–46
Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M., 2021 · 2021
Later among the works it cites.
Zhao, J., Li, D., Xiao, X., Accorsi, F., Marshall, H., Cossetto, T., Kim, D., McCarthy, D., Dawson, C., Knezevic, S., et al., 2021 · 2021
Later among the works it cites.
Improving automatic liver tumor segmentation in late-phase mri using multi-model training and 3d convolutional neural networks
Hänsch, A., Chlebus, G., Meine, H., Thielke, F., Kock, F., Paulus, T., Abolmaali, N., Schenk, A., 2022 · 2022
Closest in time.
Unetr: Transformers for 3d medical image segmentation, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D., 2022 · 2022
Closest in time.