PB-FELTuCS: Patch-Based Filtering for Enhanced Liver Tumor Classification and Segmentation
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1335-6_15
Reference21 articles.
1. Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). arXiv preprint arXiv:1901.04056 (2019)
2. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
3. Chen, Y., Hu, F., Wang, Y., Zheng, C.: Hybrid-attention densely connected U-Net with GAP for extracting livers from CT volumes. Med. Phys. 49(1), 1015–1033 (2022)
4. Chen, Y., et al.: MS-FANet: multi-scale feature attention network for liver tumor segmentation. Comput. Biol. Med. 163, 107208 (2023)
5. Chen, Y., et al.: A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans. Comput. Biol. Med. 152, 106421 (2023)
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