The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
KiU-Net: Towards Accurate Segmentation of Biomedical Images Using Over-Complete Representations
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Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
Cited by
Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
Valanarasu, J. M. J., Oza, P., Hacihaliloglu, I., & Patel, V. M. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87193-2_4 · Cited by 1,248
Self-citationMedical image segmentation using deep learning: A survey
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022) · IET Image Processing · doi:10.1049/ipr2.12419 · Cited by 892
TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation
Chen, B., Liu, Y., Zhang, Z., Lu, G., & Kong, A. W. K. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3309626 · Cited by 459
Sharp U-Net: Depthwise convolutional network for biomedical image segmentation
Zunair, H., & Ben Hamza, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104699 · Cited by 394
Metadata
- Authors
- Valanarasu, J. M. J., Sindagi, V. A., Hacihaliloglu, I., & Patel, V. M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 363-373
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3092462072
- Cited by
- 160
- References
- 5
- FWCI
- 7.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: KiU-Net: Towards Accurate Segmentation of Biomedical Images Using Over-Complete Representations. https://app.cochat.ai/papers/10.1007/978-3-030-59719-1_36
- The paper
- Valanarasu, J. M. J., Sindagi, V. A., Hacihaliloglu, I., & Patel, V. M. (2020). KiU-Net: Towards Accurate Segmentation of Biomedical Images Using Over-Complete Representations. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-59719-1_36
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
