[1]
|
Bağcı, U., Bray, M., Caban, J., Yao, J. and Mollura, D.J. (2012) Computer-Assisted Detection of Infectious Lung Diseases: A Review. Computerized Medical Imaging and Graphics, 36, 72-84. https://doi.org/10.1016/j.compmedimag.2011.06.002
|
[2]
|
Bagci, U., Yao, J., Wu, A., Caban, J., Palmore, T., Suffredini, A., Aras, O. and Mollura, D. (2012) Automatic Detection and Quantification of Tree-in-Bud (TIB) Opacities from CT Scans. IEEE Transactions on Biomedical Engineering, 59, 1620-1632. https://doi.org/10.1109/TBME.2012.2190984
|
[3]
|
Cireşan, D.C., Giusti, A., Gambardella, L.M. and Schmidhuber, J. (2013) Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. International Conference on Medical Image Computing and Computer-Assisted Intervention, Nagoya, 22-26 September 2013, 411-418.
|
[4]
|
Li, G., Wang, L., Shi, F., et al. (2013) Multi-Atlas Based Simultaneous Labeling of Longitudinal Dynamic Cortical Surfaces in Infants. Med Image Comput Comput Assist Interv, 16, 58-65.
|
[5]
|
Gao, M., Bagci, U., Lu, L., et al. (2015) Holistic Classification of CT Attenuation Patterns for Interstitial Lung Diseases via Deep Convolutional Neural Networks. Computer Methods in Biomechanics and Biomedical Engineering. Imaging & Visualization, 6, 1-6. https://doi.org/10.1080/21681163.2015.1124249
|
[6]
|
Leynes, A.P., Yang, J., Shanbhag, D.D., et al. (2017) Hybrid ZTE/Dixon MR-Based Attenuation Correction for Quantitative Uptake Estimation of Pelvic Lesions in PET/MRI. Medical Physics, 44, 902-913.
|
[7]
|
Helms, C.A. and Wall, S.D. (1985) CT Attenuation Numbers in the Lumbar Spine and Their Utility in Diagnosing Disc Disease. Computerized Radiology, 9, 291-297. https://doi.org/10.1016/0730-4862(85)90055-1
|
[8]
|
Bowen, S.R., Nyflot, M.J., Zeng, J., et al. (2013) TU-E-141-09: Impact of Attenuation Correction Mode on 4D PET/CT for Target Definition in Lung Cancer Patients. Medical Physics, 40, 449-449. https://doi.org/10.1118/1.4815437
|
[9]
|
Alshehri, S.M., Naushad, M., Ahamad, T., et al. (2014) Synthesis, Characterization of Curcumin Based Ecofriendly Antimicrobial Bio-Adsorbent for the Removal of Phenol from Aqueous Medium. Chemical Engineering Journal, 254, 181-189.
|
[10]
|
Gessert, N., Heyder, M., Latus, S., et al. (2018) Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning. The International Journal for Computer Assisted Radiology and Surgery, 13, S99-S100.
|
[11]
|
Xue, M., Tang, Y., Wu, L., et al. (2018) Model Approximation for Switched Genetic Regulatory Networks. IEEE Transactions on Neural Networks & Learning Systems, 29, 3404-3417. https://doi.org/10.1109/TNNLS.2017.2721448
|
[12]
|
Melrose, R.J., Jimenez, A.M., Riskin-Jones, H., et al. (2018) Alterations to Task Positive and Task Negative Networks during Executive Functioning in Mild Cognitive Impairment. Neuroimage Clinical, 19, 970-981. https://doi.org/10.1016/j.nicl.2018.06.014
|
[13]
|
Qu, Y., Fang, B., Zhang, W., et al. (2018) Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data. ACM Transactions on Information Systems, 37, Article No. 5. https://doi.org/10.1145/3233770
|
[14]
|
Bölcskei, H., Grohs, P., Kutyniok, G., et al. (2018) Optimal Approximation with Sparsely Connected Deep Neural Networks. SIAM Journal on Mathematics of Data Science, 1, 8-45. https://doi.org/10.1137/18M118709X
|
[15]
|
Parisi, G.I., Kemker, R., Part, J.L., et al. (2018) Continual Lifelong Learning with Neural Networks: A Review. Neural Networks, 113, 54-71. https://doi.org/10.1016/j.neunet.2019.01.012
|
[16]
|
Wu, S., Li, G., Chen, F., et al. (2018) Training and Inference with Integers in Deep Neural Networks.
|
[17]
|
Li, R., Wang, S., Zhu, F., et al. (2018) Adaptive Graph Convolutional Neural Networks.
|