Ajay N. Basavanhally, Ph.D.
Affiliations: | 2014 | Biomedical Engineering | Rutgers University, New Brunswick, New Brunswick, NJ, United States |
Area:
Biomedical Engineering, Pathology, OncologyGoogle:
"Ajay Basavanhally"Parents
Sign in to add mentor| Anant Madabhushi | grad student | 2014 | Rutgers, New Brunswick | |
| (Quantitative histomorphometry of digital pathology as a companion diagnostic: Predicting outcome for ER+ breast cancers.) | ||||
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Publications
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| McNamara G, Lucas J, Beeler JF, et al. (2020) New Technologies to Image Tumors. Cancer Treatment and Research. 180: 51-94 |
| Cruz-Roa A, Gilmore H, Basavanhally A, et al. (2018) High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. Plos One. 13: e0196828 |
| Verma N, Harding D, Mohammadi A, et al. (2018) Image-based risk score to predict recurrence of ER+ breast cancer in ECOG-ACRIN Cancer Research Group E2197. Journal of Clinical Oncology. 36: 540-540 |
| Cruz-Roa A, Gilmore H, Basavanhally A, et al. (2017) Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. Scientific Reports. 7: 46450 |
| Janowczyk A, Basavanhally A, Madabhushi A. (2016) Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society |
| Basavanhally A, Viswanath S, Madabhushi A. (2015) Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer. Plos One. 10: e0117900 |
| Cruz-Roa A, Arevalo J, Basavanhally A, et al. (2015) A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation Progress in Biomedical Optics and Imaging - Proceedings of Spie. 9287 |
| Wang H, Cruz-Roa A, Basavanhally A, et al. (2014) Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. Journal of Medical Imaging (Bellingham, Wash.). 1: 034003 |
| Wang H, Cruz-Roa A, Basavanhally A, et al. (2014) Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection Progress in Biomedical Optics and Imaging - Proceedings of Spie. 9041 |
| Cruz-Roa A, Basavanhally A, González F, et al. (2014) Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks Progress in Biomedical Optics and Imaging - Proceedings of Spie. 9041 |