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Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach.

M. Wang, J. Zhang, L. Grimm, S. Ghate, R. Walsh, K. Johnson, J. Lo, and M. Mazurowski. Expert Syst. Appl., (2016)

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Vivien Lo

Wingkwong Lo

Edward Lo

Sing Lo

Luling Lo

Other publications of authors with the same name

Application of support vector machines to breast cancer screening using mammogram and clinical history data.W. Land, D. McKee, R. Velazquez, L. Wong, J. Lo, and F. Anderson. Medical Imaging: Image Processing, volume 5032 of SPIE Proceedings, SPIE, (2003)Prediction of breast biopsy outcome using a likelihood ratio classifier and biopsy cases from two medical centers.A. Bilska-Wolak, C. Jr., and J. Lo. Medical Imaging: Image Processing, volume 5032 of SPIE Proceedings, SPIE, (2003)Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features.J. Lo, J. Kim, J. Baker, and C. Jr.. Medical Imaging: Image Processing, volume 2710 of SPIE Proceedings, SPIE, (1996)Self-organizing map for cluster analysis of a breast cancer database.M. Markey, J. Lo, G. Tourassi, and C. Jr.. Artif. Intell. Medicine, 27 (2): 113-127 (2003)Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance.M. Mazurowski, P. Habas, J. Zurada, J. Lo, J. Baker, and G. Tourassi. Neural Networks, 21 (2-3): 427-436 (2008)Self-organizing maps for analyzing mammographic findings.J. Lo, and C. Jr.. ICNN, page 2472-2474. IEEE, (1997)Development and Application of a Suite of 4-D Virtual Breast Phantoms for Optimization and Evaluation of Breast Imaging Systems.N. Kiarashi, J. Lo, Y. Lin, L. Ikejimba, S. Ghate, L. Nolte, J. Dobbins, W. Segars, and E. Samei. IEEE Trans. Medical Imaging, 33 (7): 1401-1409 (2014)Ipsilateral Lesion Detection Refinement for Tomosynthesis.Y. Ren, X. Liu, J. Ge, Z. Liang, X. Xu, L. Grimm, J. Go, J. Marks, and J. Lo. IEEE Trans. Medical Imaging, 42 (10): 3080-3090 (October 2023)Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT.F. Tushar, V. D'Anniballe, G. Rubin, E. Samei, and J. Lo. Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)Weakly Supervised Multi-Organ Multi-Disease Classification of Body CT Scans.F. Tushar, V. D'Anniballe, R. Hou, M. Mazurowski, W. Fu, E. Samei, G. Rubin, and J. Lo. CoRR, (2020)

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Disambiguation of "Lo, Joseph Y."

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Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach.

Please choose a person to relate this publication to

Vivien Lo

Wingkwong Lo

Edward Lo

Sing Lo

Luling Lo

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Lo, Joseph Y."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach.

Please choose a person to relate this publication to

Vivien Lo

Wingkwong Lo

Edward Lo

Sing Lo

Luling Lo

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach.