Schabath, Matthew B.

Radiomics: the process and the challenges

Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) · Magnetic Resonance Imaging

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

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Artificial intelligence in cancer imaging: Clinical challenges and applications

Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians

AI in cancer detectionArtificial Intelligence in Healthcare and Education

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Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma

Grove, O., Berglund, A. E., Schabath, M. B., Aerts, H. J. W. L., Dekker, A., Wang, H., Velazquez, E. R., Lambin, P., Gu, Y., Balagurunathan, Y., et al. (2015) · PLOS ONE

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

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Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization

Zhou, W., Liu, G., Hung, R. J., Haycock, P. C., Aldrich, M. C., Andrew, A. S., Arnold, S. M., Bickeböller, H., Bojesen, S. E., Brennan, P., et al. (2021) · International Journal of Cancer

Genetic Associations and EpidemiologyCancer Risks and Factors

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Radiomics Improves Cancer Screening and Early Detection

Gillies, R. J., & Schabath, M. B. (2020) · Cancer Epidemiology, Biomarkers & Prevention

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

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