Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics,14.
Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ants similarity metric performance in brain image registration. NeuroImage, 54(3), 2033–2044.
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., & Rozycki, M., et al. (2018). Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv:1811.02629
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017). Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific Data, 4(1), 1–13.
Barajas, R., Rubenstein, J., Chang, J., Hwang, J., & Cha, S. (2010). Diffusion-weighted mr imaging derived apparent diffusion coefficient is predictive of clinical outcome in primary central nervous system lymphoma. American Journal of Neuroradiology, 31(1), 60–66.
Article PubMed PubMed Central Google Scholar
Biggs, M., Wang, Y., Soni, N., Priya, S., Bathla, G., & Canahuate, G. (2023). Evaluating autoencoders for dimensionality reduction of mri-derived radiomics and classification of malignant brain tumors. In: Proceedings of the 35th international conference on scientific and statistical database management (pp. 1–11)
Billot, B., Greve, D. N., Puonti, O., Thielscher, A., Van Leemput, K., Fischl, B., Dalca, A. V., Iglesias, J. E., et al. (2023). Synthseg: Segmentation of brain mri scans of any contrast and resolution without retraining. Medical Image Analysis, 86, 102789.
Article PubMed PubMed Central Google Scholar
Calabrese, E., Rudie, J. D., Rauschecker, A. M., Villanueva-Meyer, J. E., Clarke, J. L., Solomon, D. A., & Cha, S. (2022). Combining radiomics and deep convolutional neural network features from preoperative mri for predicting clinically relevant genetic biomarkers in glioblastoma. Neuro-Oncology Advances, 4(1), 060.
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 . PMLR
Curtin, L., Whitmire, P., White, H., Bond, K. M., Mrugala, M. M., Hu, L. S., & Swanson, K. R. (2021). Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis. Scientific Reports, 11(1), 23202.
Article CAS PubMed PubMed Central Google Scholar
Destito, M., Marzullo, A., Leone, R., Zaffino, P., Steffanoni, S., Erbella, F., Calimeri, F., Anzalone, N., De Momi, E., Ferreri, A. J., et al. (2023). Radiomics-based machine learning model for predicting overall and progression-free survival in rare cancer: a case study for primary cns lymphoma patients. Bioengineering, 10(3), 285.
Article CAS PubMed PubMed Central Google Scholar
Fathi Kazerooni, A., Saxena, S., Toorens, E., Tu, D., Bashyam, V., Akbari, H., Mamourian, E., Sako, C., Koumenis, C., Verginadis, I., et al. (2022). Clinical measures, radiomics, and genomics offer synergistic value in ai-based prediction of overall survival in patients with glioblastoma. Scientific Reports, 12(1), 8784.
Article CAS PubMed PubMed Central Google Scholar
Fischl, B. (2012). Freesurfer Neuroimage, 62(2), 774–781.
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: images are more than pictures, they are data. Radiology, 278(2), 563–577.
Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics,13.
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3(1), 1–9.
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211.
Article CAS PubMed Google Scholar
Knaap, M. S., & Valk, J. (2005). Magnetic resonance of myelination and myelin disorders. Springer.
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2014). The multimodal brain tumor image segmentation benchmark (brats). IEEE Transactions on Medical Imaging, 34(10), 1993–2024.
Article PubMed PubMed Central Google Scholar
Parekh, V., & Jacobs, M. A. (2016). Radiomics: a new application from established techniques. Expert Review of Precision Medicine and Drug Development, 1(2), 207–226.
Article PubMed PubMed Central Google Scholar
Rauschecker, A. M., Rudie, J. D., Xie, L., Wang, J., Duong, M. T., Botzolakis, E. J., Kovalovich, A. M., Egan, J., Cook, T. C., Bryan, R. N., et al. (2020). Artificial intelligence system approaching neuroradiologist-level differential diagnosis accuracy at brain mri. Radiology, 295(3), 626–637.
Rudie, J.D., Weiss, R.S.D.A., Nedelec, P., Calabrese, E., Colby, J.B., Laguna, B., Mongan, J., Braunstein, S., Hess, C.P., & Rauschecker, A.M., et al. (2023). The university of california san francisco, brain metastases stereotactic radiosurgery (ucsf-bmsr) mri dataset. arXiv:2304.07248
Rudie, J. D., Rauschecker, A. M., Xie, L., Wang, J., Duong, M. T., Botzolakis, E. J., Kovalovich, A., Egan, J. M., Cook, T., Bryan, R. N., et al. (2020). Subspecialty-level deep gray matter differential diagnoses with deep learning and bayesian networks on clinical brain mri: a pilot study. Radiology Artificial Intelligence, 2(5), 190146.
Van Griethuysen, J. J., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G., Fillion-Robin, J.-C., Pieper, S., & Aerts, H. J. (2017). Computational radiomics system to decode the radiographic phenotype. Cancer Research, 77(21), 104–107.
Weiss, D. A., Saluja, R., Xie, L., Gee, J. C., Sugrue, L. P., Pradhan, A., Bryan, R. N., Rauschecker, A. M., & Rudie, J. D. (2021). Automated multiclass tissue segmentation of clinical brain mris with lesions. NeuroImage Clinical, 31, 102769.
Article PubMed PubMed Central Google Scholar
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006). User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage, 31(3), 1116–1128.
Yushkevich, P. A., Pluta, J., Wang, H., Wisse, L. E., Das, S., & Wolk, D. (2016). Ic-p-174: fast automatic segmentation of hippocampal subfields and medial temporal lobe subregions in 3 tesla and 7 tesla t2-weighted mri. Alzheimer’s & Dementia, 12, 126–127.
Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S. (2021). Barlow twins: Self-supervised learning via redundancy reduction. In: International conference on machine learning (pp. 12310–12320). PMLR
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