The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Combo loss: Handling input and output imbalance in multi-organ segmentation
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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
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
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
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
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
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599
Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations
Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., & Jorge Cardoso, M. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-67558-9_28 · Cited by 2,829
Cited by
Medical 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
Deep semantic segmentation of natural and medical images: a review
Asgari Taghanaki, S., Abhishek, K., Cohen, J. P., Cohen-Adad, J., & Hamarneh, G. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09854-1 · Cited by 870
Self-citationYeung, M., Sala, E., Schönlieb, C.-B., & Rundo, L. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.102026 · Cited by 733
Loss odyssey in medical image segmentation
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662
Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07953-4 · Cited by 188
Metadata
- Authors
- Taghanaki, S. A., Zheng, Y., Kevin Zhou, S., Georgescu, B., Sharma, P., Xu, D., Comaniciu, D., & Hamarneh, G.
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 75
- Pages
- 24-33
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W2801459697
- Cited by
- 458
- References
- 15
- FWCI
- 16.54
- Type
- journal article
- Language
- en
- Open access
- green
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- Taghanaki, S. A., Zheng, Y., Kevin Zhou, S., Georgescu, B., Sharma, P., Xu, D., Comaniciu, D., & Hamarneh, G. (2019). Combo loss: Handling input and output imbalance in multi-organ segmentation. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2019.04.005
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