Giger, Maryellen L.

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

Cited by 1,846

Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update

Tejani, A. S., Klontzas, M. E., Gatti, A. A., Mongan, J. T., Moy, L., Park, S. H., Kahn, C. E., Panel, F. T. C. 2. U., Abbara, S., Afat, S., et al. (2024) · Radiology: Artificial Intelligence

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 570

A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets

Antropova, N., Huynh, B. Q., & Giger, M. L. (2017) · Medical Physics

Radiomics and Machine Learning in Medical ImagingAI in cancer detection

Cited by 389

Anniversary Paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM

Giger, M. L., Chan, H., & Boone, J. (2008) · Medical Physics

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 311

Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma

Zhu, Y., Li, H., Guo, W., Drukker, K., Lan, L., Giger, M. L., & Ji, Y. (2015) · Scientific Reports

Radiomics and Machine Learning in Medical ImagingMRI in cancer diagnosis

Cited by 165

Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.

Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 149

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

Guo, W., Li, H., Zhu, Y., Lan, L., Yang, S., Drukker, K., Morris, E. A., Burnside, E. S., Whitman, G. J., Giger, M. L., et al. (2015) · Journal of Medical Imaging

Radiomics and Machine Learning in Medical ImagingBreast Cancer Treatment Studies

Cited by 148

Evaluating Imaging and Computer-aided Detection and Diagnosis Devices at the FDA

Gallas, B. D., Chan, H.-P., D’Orsi, C. J., Dodd, L. E., Giger, M. L., Gur, D., Krupinski, E. A., Metz, C. E., Myers, K. J., Obuchowski, N. A., et al. (2012) · Academic Radiology

Artificial Intelligence in Healthcare and EducationRadiology practices and education

Cited by 81

Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage

Burnside, E. S., Drukker, K., Li, H., Bonaccio, E., Zuley, M., Ganott, M., Net, J. M., Sutton, E. J., Brandt, K. R., Whitman, G. J., et al. (2016) · Cancer

MRI in cancer diagnosisRadiomics and Machine Learning in Medical Imaging

Cited by 71