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Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma

Publication ,  Conference
Li, H; Sheth, D; Mendel, KR; Lan, L; Giger, ML
Published in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
January 1, 2018

We investigated the additive role of breast parenchyma stroma in the computer-aided diagnosis (CADx) of tumors on full-field digital mammograms (FFDM) by combining images of the tumor and contralateral normal parenchyma information via deep learning. The study included 182 breast lesions in which 106 were malignant and 76 were benign. All FFDM images were acquired using a GE 2000D Senographe system and retrospectively collected under an Institution Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant protocol. Convolutional neutral networks (CNNs) with transfer learning were used to extract image-based characteristics of lesions and of parenchymal patterns (on the contralateral breast) directly from the FFDM images. Classification performance was evaluated and compared between analysis of only tumors and that of combined tumor and parenchymal patterns in the task of distinguishing between malignant and benign cases with the area under the Receiver Operating Characteristic (ROC) curve (AUC) used as the figure of merit. Using only lesion image data, the transfer learning method yielded an AUC value of 0.871 (SE=0.025) and using combined information from both lesion and parenchyma analyses, an AUC value of 0.911 (SE=0.021) was observed. This improvement was statistically significant (p-value=0.0362). Thus, we conclude that using CNNs with transfer learning to combine extracted image information of both tumor and parenchyma may improve breast cancer diagnosis.

Duke Scholars

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

ISBN

9781510620070

Publication Date

January 1, 2018

Volume

10718
 

Citation

APA
Chicago
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MLA
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Li, H., Sheth, D., Mendel, K. R., Lan, L., & Giger, M. L. (2018). Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 10718). https://doi.org/10.1117/12.2318282
Li, H., D. Sheth, K. R. Mendel, L. Lan, and M. L. Giger. “Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma.” In Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 10718, 2018. https://doi.org/10.1117/12.2318282.
Li H, Sheth D, Mendel KR, Lan L, Giger ML. Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Li, H., et al. “Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma.” Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10718, 2018. Scopus, doi:10.1117/12.2318282.
Li H, Sheth D, Mendel KR, Lan L, Giger ML. Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

ISBN

9781510620070

Publication Date

January 1, 2018

Volume

10718