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Andrei S. Purysko
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2020 – today
- 2023
- [c9]Thomas DeSilvio, Leo Bao, Dhruv Seth, Prathyush Chirra, Sneha Singh
, Atreya Sridharan, Murad Labbad, Katherine Bingmer, Diana Jodeh, Eric L. Marderstein, Rajmohan Paspulati, David Liska, Kenneth Friedman, Smitha S. Krishnamurthi, Sharon L. Stein, Andrei S. Purysko, Satish E. Viswanath
Integrating multi-plane and multi-region radiomic features to predict pathologic response to neoadjuvant chemoradiation in rectal cancers via pre-treatment MRI. Image-Guided Procedures 2023 - 2022
- [c8]Ansh Roge
Evaluating the sensitivity of deep learning to inter-reader variations in lesion delineations on bi-parametric MRI in identifying clinically significant prostate cancer. Computer-Aided Diagnosis 2022 - [c7]Amir Reza Sadri, Thomas DeSilvio, Prathyush Chirra, Andrei S. Purysko, Rajmohan Paspulati, Kenneth Friedman, Smitha S. Krishnamurthi, David Liska, Sharon L. Stein, Satish E. Viswanath
Deep hybrid convolutional wavelet networks: application to predicting response to chemoradiation in rectal cancers via MRI. Computer-Aided Diagnosis 2022 - 2020
- [c6]Amogh Hiremath, Rakesh Shiradkar, Nathaniel Braman, Prateek Prasanna
A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer. Computer-Aided Diagnosis 2020 - [c5]Amrish Selvam, Jacob Antunes, Kaustav Bera, Asya Ofshteyn, Justin T. Brady, Katherine Bingmer, Kenneth Friedman, Sharon L. Stein, Rajmohan Paspulati, Andrei S. Purysko, Matthew Kalady, Anant Madabhushi, Satish E. Viswanath
Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers. Computer-Aided Diagnosis 2020
2010 – 2019
- 2019
- [j1]Satish Viswanath
Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study. BMC Medical Imaging 19(1): 22:1-22:12 (2019) - [c4]Lin Li, Rakesh Shiradkar, Ahmad Algohary, Patrick Leo
Radiomic features derived from pre-operative multi-parametric MRI of prostate cancer are associated with Decipher risk score. Computer-Aided Diagnosis 2019: 109503Y - [c3]Michael C. Yim
Radiomic characterization of perirectal fat on MRI enables accurate assessment of tumor regression and lymph node metastasis in rectal cancers after chemoradiation. Image-Guided Procedures 2019: 109512A - 2018
- [c2]Prathyush Chirra, Patrick Leo
Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI. Computer-Aided Diagnosis 2018: 105750B - 2017
- [c1]Soumya Ghose, Rakesh Shiradkar, Mirabela Rusu, Jhimli Mitra, Rajat Thawani
Field Effect Induced Organ Distension (FOrge) Features Predicting Biochemical Recurrence from Pre-treatment Prostate MRI. MICCAI (2) 2017: 442-449
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last updated on 2024-11-28 21:27 CET by the dblp team
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