A systematic study of the class imbalance problem in convolutional neural networks von Buda, Mateusz, Maki, Atsuto, Mazurowski, Maciej A.

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Radiogenomics: What It Is and Why It Is Important von Mazurowski, Maciej A., PhD

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Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing von AlBadawy, Ehab A., Saha, Ashirbani, Mazurowski, Maciej A.

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Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set von Cain, Elizabeth Hope, Saha, Ashirbani, Harowicz, Michael R., Marks, Jeffrey R., Marcom, P. Kelly, Mazurowski, Maciej A.

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Effects of MRI scanner parameters on breast cancer radiomics von Saha, Ashirbani, Yu, Xiaozhi, Sahoo, Dushyant, Mazurowski, Maciej A.

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A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis von Swiecicki, Albert, Konz, Nicholas, Buda, Mateusz, Mazurowski, Maciej A.

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Segment anything model for medical image analysis: An experimental study von Mazurowski, Maciej A., Dong, Haoyu, Gu, Hanxue, Yang, Jichen, Konz, Nicholas, Zhang, Yixin

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Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance von Mazurowski, Maciej A., Habas, Piotr A., Zurada, Jacek M., Lo, Joseph Y., Baker, Jay A., Tourassi, Georgia D.

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How accurate and precise are CT based measurements of iodine concentration? A comparison of the minimum detectable concentration difference among single source and dual source dual... von Euler, André, Solomon, Justin, Mazurowski, Maciej A., Samei, Ehsan, Nelson, Rendon C.

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Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm von Buda, Mateusz, Saha, Ashirbani, Mazurowski, Maciej A.

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Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study von Czarnek, Nicholas, Clark, Kal, Peters, Katherine B., Mazurowski, Maciej A.

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Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI von Mazurowski, Maciej A., Buda, Mateusz, Saha, Ashirbani, Bashir, Mustafa R.

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Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ von Zhu, Zhe, Harowicz, Michael, Zhang, Jun, Saha, Ashirbani, Grimm, Lars J., Hwang, E. Shelley, Mazurowski, Maciej A.

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A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI von Lew, Christopher O., Harouni, Majid, Kirksey, Ella R., Kang, Elianne J., Dong, Haoyu, Gu, Hanxue, Grimm, Lars J., Walsh, Ruth, Lowell, Dorothy A., Mazurowski, Maciej A.

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A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images von Buda, Mateusz, Saha, Ashirbani, Walsh, Ruth, Ghate, Sujata, Li, Nianyi, Swiecicki, Albert, Lo, Joseph Y, Mazurowski, Maciej A

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Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce von Mazurowski, Maciej A.

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Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features von Shi, Bibo, Grimm, Lars J., Mazurowski, Maciej A., Baker, Jay A., Marks, Jeffrey R., King, Lorraine M., Maley, Carlo C., Hwang, E. Shelley, Lo, Joseph Y.

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Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics von Zhang, Jun, Saha, Ashirbani, Zhu, Zhe, Mazurowski, Maciej A.

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Artificial Intelligence in Radiology: Some Ethical Considerations for Radiologists and Algorithm Developers von Mazurowski, Maciej A.

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Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning von D'Anniballe, Vincent M, Tushar, Fakrul Islam, Faryna, Khrystyna, Han, Songyue, Mazurowski, Maciej A, Rubin, Geoffrey D, Lo, Joseph Y

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