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<meta name="citation_doi" content="10.7937/K9/TCIA.2016.8LNG8XDR" />
<meta name="citation_title" content=" Radiology Data from The Cancer Genome Atlas Urothelial Bladder Carcinoma [TCGA-BLCA] collection." /> |
- Cai, Y., Li, Y., Qiu, C., Ma, J., & Gao, X. (2019). Medical Image Retrieval Based on Convolutional Neural Network and Supervised Hashing. IEEE Access, 7, 51877-51885. doi: https://doi.org/10.1109/ACCESS.2019.2911630
- Du, R., & Vardhanabhuti, V. (2020, 06-08 July 2020). 3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks. Paper presented at the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), Montréal, QC, Canada. PMLR 121:174-192, 2020.
- Lin, P., Wen, D.-Y., Chen, L., Li, X., Li, S.-H., Yan, H.-B., . . . Yang, H. (2019). A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma. Eur Radiol. doi:10.1007/s00330-019-06371-w
- Moitra, D., & Mandal, R. K. (2022). Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model. Multimed Tools Appl, 1-19. doi: https://doi.org/10.1007/s11042-022-12229-z
- Moshfeghifar, F., Gholamalizadeh, T., Ferguson, Z., Schneider, T., Nielsen, M. B., Panozzo, D., . . . Erleben, K. (2022). LibHip: An open-access hip joint model repository suitable for finite element method simulation. Computer Methods and Programs in Biomedicine, 226, 107140. doi: https://doi.org/10.1016/j.cmpb.2022.107140
- Pinto, J. R., & Tavares, J. M. R. (2017). A versatile method for bladder segmentation in computed tomography two-dimensional images under adverse conditions. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 1-8. doi: https://doi.org/10.1177/0954411917714294
- Wong, J., Fong, A., McVicar, N., Smith, S., Giambattista, J., Wells, D., . . . Alexander, A. (2019). Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol, 144, 152-158. doi: https://doi.org/10.1016/j.radonc.2019.10.019
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