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Improving the repeatability of deep learning models with Monte Carlo dropout.

A. Lemay, K. Hoebel, C. Bridge, B. Befano, S. Sanjosé, D. Egemen, A. Rodriguez, M. Schiffman, J. Campbell, and J. Kalpathy-Cramer. npj Digit. Medicine, (2022)

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Mark Lipschitz

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Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic PipelinesK. Ellrott, M. Bailey, G. Saksena, K. Covington, C. Kandoth, C. Stewart, J. Hess, S. Ma, K. Chiotti, M. McLellan and 730 other author(s). Cell Systems, 6 (3): 271--281.e7 (March 2018)Improving the repeatability of deep learning models with Monte Carlo dropout.A. Lemay, K. Hoebel, C. Bridge, B. Befano, S. Sanjosé, D. Egemen, A. Rodriguez, M. Schiffman, J. Campbell, and J. Kalpathy-Cramer. npj Digit. Medicine, (2022)A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer.S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and L. Long. Image Processing, volume 5370 of SPIE Proceedings, SPIE, (2004)Image Augmentation for Improving Automated Eligibility-Classification for Cervical Precancer Ablation Treatment.P. Guo, Z. Xue, J. Jeronimo, J. Gage, K. Desai, B. Befano, F. García, M. Schiffman, and S. Antani. RTIP2R, volume 1576 of Communications in Computer and Information Science, page 76-84. Springer, (2021)A Deep Clustering Method For Analyzing Uterine Cervix Images Across Imaging Devices.Z. Xue, P. Guo, K. Desai, A. Pal, K. Ajenifuja, C. Adepiti, L. Long, M. Schiffman, and S. Antani. CBMS, page 527-532. IEEE, (2021)Conformal Prediction and Monte Carlo Inference for Addressing Uncertainty in Cervical Cancer Screening.C. Clark, S. Kinder, D. Egemen, B. Befano, K. Desai, S. Ahmed, P. Singh, A. Rodriguez, J. Jeronimo, S. Sanjosé and 3 other author(s). UNSURE@MICCAI, volume 15167 of Lecture Notes in Computer Science, page 205-214. Springer, (2024)Image Quality Classification for Automated Visual Evaluation of Cervical Precancer.Z. Xue, S. Angara, P. Guo, S. Rajaraman, J. Jeronimo, A. Rodriguez, K. Alfaro, K. Charoenkwan, C. Mungo, J. Domgue and 8 other author(s). MILLanD@MICCAI, volume 13559 of Lecture Notes in Computer Science, page 206-217. Springer, (2022)Technology for Medical Education, Research, and Disease Screening by Exploitation of Biomarkers in a Large Collection of Uterine Cervix Images.L. Long, S. Antani, J. Jeronimo, M. Schiffman, M. Bopf, L. Neve, C. Cornwell, S. Budihas, and G. Thoma. CBMS, page 826-831. IEEE Computer Society, (2006)Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 PatientsA. Kahles, K. Lehmann, N. Toussaint, M. Hüser, S. Stark, T. Sachsenberg, O. Stegle, O. Kohlbacher, C. Sander, S. Caesar-Johnson and 725 other author(s). Cancer Cell, 34 (2): 211--224.e6 (August 2018)Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic PipelinesK. Ellrott, M. Bailey, G. Saksena, K. Covington, C. Kandoth, C. Stewart, J. Hess, S. Ma, K. Chiotti, M. McLellan and 730 other author(s). Cell Systems, 6 (3): 271--281.e7 (March 2018)

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Disambiguation of "Schiffman, Mark"

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Improving the repeatability of deep learning models with Monte Carlo dropout.

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Mark Yampolsky

Mark Schütte

Mark Yampolskiy

Ana Markovi

Mark Lipschitz

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Disambiguation of "Schiffman, Mark"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Improving the repeatability of deep learning models with Monte Carlo dropout.

Please choose a person to relate this publication to

Mark Yampolsky

Mark Schütte

Mark Yampolskiy

Ana Markovi

Mark Lipschitz

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Disambiguation

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Improving the repeatability of deep learning models with Monte Carlo dropout.