Deep Learning

Displaying 1 - 44 of 44CSV
Hathaway, Q. A., Kasaeian, A., Pan, T., Bluemke, D. A., Ghotbi, E., Klein, J. G., Ibad, H. A., Dailing, C., Tison, G. H., Barr, R. G., Post, W., Allison, M., Lima, J. A. C., Budoff, M., & Demehri, S. (2025). A Deep Learning Model for Three-Dimensional Determination of Whole Thoracic Vertebral Bone Mineral Density from Noncontrast Chest CT: The Multi-Ethnic Study of Atherosclerosis. Radiology, 314(3). https://doi.org/10.1148/radiol.242133
Publication Date
Ahluwalia, V. S., Doiphode, N., Mankowski, W. C., Cohen, E. A., Pati, S., Pantalone, L., Bakas, S., Brooks, A., Vachon, C. M., Conant, E. F., Gastounioti, A., & Kontos, D. (2024). Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning. JCO Clinical Cancer Informatics, 8. https://doi.org/10.1200/cci.24.00103
Publication Date
Wang, F., Zou, Z., Sakla, N., Partyka, L., Rawal, N., Singh, G., Zhao, W., Ling, H., Huang, C., Prasanna, P., & Chen, C. (2025). TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs. Medical Image Analysis, 99, 103373. https://doi.org/10.1016/j.media.2024.103373
Publication Date
Manso Jimeno, M., Ravi, K. S., Fung, M., Oyekunle, D., Ogbole, G., Vaughan, J. T., & Geethanath, S. (2024). Automated detection of motion artifacts in brain MR images using deep learning. NMR in Biomedicine, 38(1). Portico. https://doi.org/10.1002/nbm.5276
Publication Date
Tian, Y., Sharma, A., Mehta, S., Kaushal, S., Liebmann, J. M., Cioffi, G. A., & Thakoor, K. A. (2024). Automated Identification of Clinically Relevant Regions in Glaucoma OCT Reports Using Expert Eye Tracking Data and Deep Learning. Translational Vision Science & Technology, 13(10), 24. https://doi.org/10.1167/tvst.13.10.24
Publication Date
Link, K. E., Schnurman, Z., Liu, C., Kwon, Y. J., Jiang, L. Y., Nasir-Moin, M., Neifert, S., Alzate, J. D., Bernstein, K., Qu, T., Chen, V., Yang, E., Golfinos, J. G., Orringer, D., Kondziolka, D., & Oermann, E. K. (2024). Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-52414-2
Publication Date
Wu, J.-H., Nishida, T., & Liu, T. Y. A. (2024). Accuracy of large language models in answering ophthalmology board-style questions: A meta-analysis. Asia-Pacific Journal of Ophthalmology, 13(5), 100106. https://doi.org/10.1016/j.apjo.2024.100106
Publication Date
Kessler, D., Zhu, M., Gregory, C. R., Mehanian, C., Avila, J., Avitable, N., Coneybeare, D., Das, D., Dessie, A., Kennedy, T. M., Rabiner, J., Malia, L., Ng, L., Nye, M., Vindas, M., Weimersheimer, P., Kulhare, S., Millin, R., Gregory, K., … Lancioni, C. (2024). Development and testing of a deep learning algorithm to detect lung consolidation among children with pneumonia using hand-held ultrasound. PLOS ONE, 19(8), e0309109. https://doi.org/10.1371/journal.pone.0309109
Publication Date
Shi, M., Lokhande, A., Tian, Y., Luo, Y., Eslami, M., Kazeminasab, S., Elze, T., Shen, L. Q., Pasquale, L. R., Wellik, S. R., De Moraes, C. G., Myers, J. S., Zebardast, N., Friedman, D. S., Boland, M. V., & Wang, M. (2024). Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data. Translational Vision Science & Technology, 13(8), 11. https://doi.org/10.1167/tvst.13.8.11
Publication Date
Wang, S. J., Hu, Z., Li, C., He, X., Zhu, C., Wang, Y., Sattar, U., Bazojoo, V., He, H. Y. N., Blumenfeld, J. D., & Prince, M. R. (2024). Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning. Tomography, 10(7), 1148–1158. https://doi.org/10.3390/tomography10070087
Publication Date
Matsubayashi, C. O., Cheng, S., Hulchafo, I., Zhang, Y., Tada, T., Buxbaum, J. L., & Ochiai, K. (2024). Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market. Digestive and Liver Disease, 56(7), 1156–1163. https://doi.org/10.1016/j.dld.2024.04.019
Publication Date
Columbia Affiliation
McGuinness, J. E., Anderson, G. L., Mutasa, S., Hershman, D. L., Terry, M. B., Tehranifar, P., Lew, D. L., Yee, M., Brown, E. A., Kairouz, S. S., Kuwajerwala, N., Bevers, T. B., Doster, J. E., Zarwan, C., Kruper, L., Minasian, L. M., Ford, L., Arun, B., Neuhouser, M. L., … Crew, K. D. (2024). Effects of vitamin D supplementation on a deep learning–based mammographic evaluation in SWOG S0812. JNCI Cancer Spectrum, 8(4). https://doi.org/10.1093/jncics/pkae042
Publication Date
Santos, L., Hsu, H.-Y., Nelson, R. R., Sullivan, B., Shin, J., Fung, M., Lebel, M. R., Jambawalikar, S., & Jaramillo, D. (2024). Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions. Tomography, 10(4), 504–519. https://doi.org/10.3390/tomography10040039
Publication Date
Bhave, S., Rodriguez, V., Poterucha, T., Mutasa, S., Aberle, D., Capaccione, K. M., Chen, Y., Dsouza, B., Dumeer, S., Goldstein, J., Hodes, A., Leb, J., Lungren, M., Miller, M., Monoky, D., Navot, B., Wattamwar, K., Wattamwar, A., Clerkin, K., … Elias, P. (2024). Deep learning to detect left ventricular structural abnormalities in chest X-rays. European Heart Journal, 45(22), 2002–2012. https://doi.org/10.1093/eurheartj/ehad782
Publication Date
Ma, D. J., Yang, Y., Harguindeguy, N., Tian, Y., Small, S. A., Liu, F., Rothman, D. L., & Guo, J. (2023). Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning. Journal of Magnetic Resonance Imaging, 59(3), 964–975. Portico. https://doi.org/10.1002/jmri.28868
Publication Date
Chuter, B., Huynh, J., Bowd, C., Walker, E., Rezapour, J., Brye, N., Belghith, A., Fazio, M. A., Girkin, C. A., De Moraes, G., Liebmann, J. M., Weinreb, R. N., Zangwill, L. M., & Christopher, M. (2024). Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection. Translational Vision Science & Technology, 13(1), 23. https://doi.org/10.1167/tvst.13.1.23
Publication Date
Rayn, K., Gupta, V., Mulinti, S., Clark, R., Magliari, A., Chaudhari, S., Garima, G., & Beriwal, S. (2024). Evaluation of a deep image-to-image network (DI2IN) auto-segmentation algorithm across a network of cancer centers. Journal of Cancer Research and Therapeutics, 20(3), 1020–1025. https://doi.org/10.4103/jcrt.jcrt_769_23
Publication Date
Ouyang, D., Theurer, J., Stein, N. R., Hughes, J. W., Elias, P., He, B., Yuan, N., Duffy, G., Sandhu, R. K., Ebinger, J., Botting, P., Jujjavarapu, M., Claggett, B., Tooley, J. E., Poterucha, T., Chen, J. H., Nurok, M., Perez, M., Perotte, A., … Albert, C. M. (2024). Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study. The Lancet Digital Health, 6(1), e70–e78. https://doi.org/10.1016/s2589-7500(23)00220-0
Publication Date
Kampaktsis, P. N., Bohoran, T. A., Lebehn, M., McLaughlin, L., Leb, J., Liu, Z., Moustakidis, S., Siouras, A., Singh, A., Hahn, R. T., McCann, G. P., & Giannakidis, A. (2023). An attention‐based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy. Echocardiography, 41(1). Portico. https://doi.org/10.1111/echo.15719
Publication Date
Ibrahim, A., Vaidyanathan, A., Primakov, S., Belmans, F., Bottari, F., Refaee, T., Lovinfosse, P., Jadoul, A., Derwael, C., Hertel, F., Woodruff, H. C., Zacho, H. D., Walsh, S., Vos, W., Occhipinti, M., Hanin, F.-X., Lambin, P., Mottaghy, F. M., & Hustinx, R. (2023). Deep learning based identification of bone scintigraphies containing metastatic bone disease foci. Cancer Imaging, 23(1). https://doi.org/10.1186/s40644-023-00524-3
Publication Date
Wang, Y., Wang, W., Liu, D., Hou, W., Zhou, T., & Ji, Z. (2023). GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging. Genome Biology, 24(1). https://doi.org/10.1186/s13059-023-03054-0
Publication Date
Ibad, H. A., Hathaway, Q. A., Bluemke, D. A., Kasaeian, A., Klein, J. G., Budoff, M. J., Barr, R. G., Allison, M., Post, W. S., Lima, J. A. C., & Demehri, S. (2023). CT-derived pectoralis composition and incident pneumonia hospitalization using fully automated deep-learning algorithm: multi-ethnic study of atherosclerosis. European Radiology, 34(6), 4163–4175. https://doi.org/10.1007/s00330-023-10372-1
Publication Date
He, X., Hu, Z., Dev, H., Romano, D. J., Sharbatdaran, A., Raza, S. I., Wang, S. J., Teichman, K., Shih, G., Chevalier, J. M., Shimonov, D., Blumenfeld, J. D., Goel, A., Sabuncu, M. R., & Prince, M. R. (2024). Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning. Academic Radiology, 31(3), 889–899. https://doi.org/10.1016/j.acra.2023.09.009
Publication Date
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Hanikel, N., Pellock, S. J., Courbet, A., Sheffler, W., Wang, J., Venkatesh, P., Sappington, I., Torres, S. V., … Baker, D. (2023). De novo design of protein structure and function with RFdiffusion. Nature, 620(7976), 1089–1100. https://doi.org/10.1038/s41586-023-06415-8
Publication Date
Megjhani, M., Terilli, K., Weinerman, B., Nametz, D., Kwon, S. B., Velazquez, A., Ghoshal, S., Roh, D. J., Agarwal, S., Connolly, E. S., Claassen, J., & Park, S. (2023). A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler. Annals of Neurology, 94(1), 196–202. Portico. https://doi.org/10.1002/ana.26682
Publication Date
Siddique, M., Liu, M., Duong, P., Jambawalikar, S., & Ha, R. (2023). Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review. Tomography, 9(3), 1110–1119. https://doi.org/10.3390/tomography9030091
Publication Date
Zech, J. R., Carotenuto, G., Igbinoba, Z., Tran, C. V., Insley, E., Baccarella, A., & Wong, T. T. (2023). Detecting pediatric wrist fractures using deep-learning-based object detection. Pediatric Radiology, 53(6), 1125–1134. https://doi.org/10.1007/s00247-023-05588-8
Publication Date
Ahmed, F., Kang, I. S., Kim, K. H., Asif, A., Rahim, C. S. A., Samantasinghar, A., Memon, F. H., & Choi, K. H. (2023). Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches. Journal of Medical Virology, 95(4). Portico. https://doi.org/10.1002/jmv.28693
Publication Date
Christopher, M., Hoseini, P., Walker, E., Proudfoot, J. A., Bowd, C., Fazio, M. A., Girkin, C. A., De Moraes, C. G., Liebmann, J. M., Weinreb, R. N., Schwartzman, A., Zangwill, L. M., & Welsbie, D. S. (2023). A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design. Ophthalmology Glaucoma, 6(2), 147–159. https://doi.org/10.1016/j.ogla.2022.08.014
Publication Date
Kamalipour, A., Moghimi, S., Khosravi, P., Jazayeri, M. S., Nishida, T., Mahmoudinezhad, G., Li, E. H., Christopher, M., Liebmann, J. M., Fazio, M. A., Girkin, C. A., Zangwill, L., & Weinreb, R. N. (2023). Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements. American Journal of Ophthalmology, 246, 163–173. https://doi.org/10.1016/j.ajo.2022.10.013
Publication Date
Singh, A., Miller, R. J. H., Otaki, Y., Kavanagh, P., Hauser, M. T., Tzolos, E., Kwiecinski, J., Van Kriekinge, S., Wei, C.-C., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., … Slomka, P. J. (2023). Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning. JACC: Cardiovascular Imaging, 16(2), 209–220. https://doi.org/10.1016/j.jcmg.2022.07.017
Publication Date
Liu, X., Mao, J., Sun, N., Yu, X., Chai, L., Tian, Y., Wang, J., Liang, J., Tao, H., Yuan, L., Lu, J., Wang, Y., Zhang, B., Wu, K., Wang, Y., Chen, M., Wang, Z., & Lu, L. (2022). Deep Learning for Detection of Intracranial Aneurysms from Computed Tomography Angiography Images. Journal of Digital Imaging, 36(1), 114–123. https://doi.org/10.1007/s10278-022-00698-5
Publication Date
Duong, T. T. H., Uher, D., Young, S. D., Farooquee, R., Druffner, A., Pasternak, A., Kanner, C., Fragala-Pinkham, M., Montes, J., & Zanotto, D. (2023). Accurate COP Trajectory Estimation in Healthy and Pathological Gait Using Multimodal Instrumented Insoles and Deep Learning Models. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 4801–4811. https://doi.org/10.1109/tnsre.2023.3338519
Publication Date
Miller, R. J. H., Singh, A., Otaki, Y., Tamarappoo, B. K., Kavanagh, P., Parekh, T., Hu, L.-H., Gransar, H., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M. F., Liang, J. X., … Slomka, P. J. (2022). Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images. European Journal of Nuclear Medicine and Molecular Imaging, 50(2), 387–397. https://doi.org/10.1007/s00259-022-05972-w
Publication Date
Anandakumaran, P. N., Ayers, A. G., Muranski, P., Creusot, R. J., & Sia, S. K. (2022). Rapid video-based deep learning of cognate versus non-cognate T cell-dendritic cell interactions. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-021-04286-5
Publication Date
Miller, R. J. H., Kuronuma, K., Singh, A., Otaki, Y., Hayes, S., Chareonthaitawee, P., Kavanagh, P., Parekh, T., Tamarappoo, B. K., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T., Dorbala, S., Di Carli, M. F., … Slomka, P. J. (2022). Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging. Journal of Nuclear Medicine, jnumed.121.263686. https://doi.org/10.2967/jnumed.121.263686
Publication Date
Lee, J., Liu, C., Kim, J., Chen, Z., Sun, Y., Rogers, J. R., Chung, W. K., & Weng, C. (2022). Deep learning for rare disease: A scoping review. Journal of Biomedical Informatics, 135, 104227. https://doi.org/10.1016/j.jbi.2022.104227
Publication Date
Dumais, F., Caceres, M. P., Janelle, F., Seifeldine, K., Arès-Bruneau, N., Gutierrez, J., Bocti, C., & Whittingstall, K. (2022). eICAB: A novel deep learning pipeline for Circle of Willis multiclass segmentation and analysis. NeuroImage, 260, 119425. https://doi.org/10.1016/j.neuroimage.2022.119425
Publication Date
Elias, P., Poterucha, T. J., Rajaram, V., Moller, L. M., Rodriguez, V., Bhave, S., Hahn, R. T., Tison, G., Abreau, S. A., Barrios, J., Torres, J. N., Hughes, J. W., Perez, M. V., Finer, J., Kodali, S., Khalique, O., Hamid, N., Schwartz, A., Homma, S., … Perotte, A. J. (2022). Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease. Journal of the American College of Cardiology, 80(6), 613–626. https://doi.org/10.1016/j.jacc.2022.05.029
Publication Date
Lemus, O. M. D., Wang, Y., Li, F., Jambawalikar, S., Horowitz, D. P., Xu, Y., & Wuu, C. (2022). Dosimetric assessment of patient dose calculation on a deep learning‐based synthesized computed tomography image for adaptive radiotherapy. Journal of Applied Clinical Medical Physics, 23(7). Portico. https://doi.org/10.1002/acm2.13595
Publication Date
Park, J., Artin, M. G., Lee, K. E., Pumpalova, Y. S., Ingram, M. A., May, B. L., Park, M., Hur, C., & Tatonetti, N. P. (2022). Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer. Journal of Biomedical Informatics, 131, 104095. https://doi.org/10.1016/j.jbi.2022.104095
Publication Date
Manso Jimeno, M., Ravi, K. S., Jin, Z., Oyekunle, D., Ogbole, G., & Geethanath, S. (2022). ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning. Magnetic Resonance Imaging, 89, 42–48. https://doi.org/10.1016/j.mri.2022.02.002
Publication Date
Fan, R., Bowd, C., Christopher, M., Brye, N., Proudfoot, J. A., Rezapour, J., Belghith, A., Goldbaum, M. H., Chuter, B., Girkin, C. A., Fazio, M. A., Liebmann, J. M., Weinreb, R. N., Gordon, M. O., Kass, M. A., Kriegman, D., & Zangwill, L. M. (2022). Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning. JAMA Ophthalmology, 140(4), 383. https://doi.org/10.1001/jamaophthalmol.2022.0244
Publication Date