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ERRATUM article

Front. Oncol., 29 April 2021
Sec. Cancer Imaging and Image-directed Interventions

Erratum: Improving the Prediction of Benign or Malignant Breast Masses Using a Combination of Image Biomarkers and Clinical Parameters

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An Erratum on
Improving the Prediction of Benign or Malignant Breast Masses Using a Combination of Image Biomarkers and Clinical Parameters

By Cui Y, Li Y, Xing D, Bai T, Dong J and Zhu J (2021). Front. Oncol. 11:629321. doi: 10.3389/fonc.2021.629321

Due to a production error, in the original article, references (115) were incorrectly ordered. The correct order and references are provided below. The publisher apologizes for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

References

1. Raza S, Goldkamp AL, Chikarmane SA, Birdwell RL. US of Breast Masses Categorized as BI-RADS 3, 4, and 5: Pictorial Review of Factors Influencing Clinical Management. Radiographics (2010) 30:1199. doi: 10.1148/rg.305095144

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scaleimage Recognition. In: Cvpr. Piscataway, NJ: IEEE (2014).

Google Scholar

3. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. Imagenet Large Scale Visual Recognition Challenge. Int J Comput Vision (2015) 115:211–52. doi: 10.1007/s11263-015-0816-y

CrossRef Full Text | Google Scholar

4. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. In: Cvpr. Las Vegas, NV (2016). p. 2818–26. doi: 10.1109/CVPR.2016.308

CrossRef Full Text | Google Scholar

5. Yuan Y, Chao M, Lo YC. Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance. IEEE Trans Med Imaging (2017) 36:1876–86. doi: 10.1109/TMI.2017.2695227

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Gal Y, Ghahramani Z. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In: Icml. New York, NY (2016). p. 1050–9.

Google Scholar

7. Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, et al. Texture-Based Classificationof Different Single Liver Lesion Based on SPAIR T2w MRI Images. BMC MedImaging (2017) 17:1–9. doi: 10.1186/s12880-017-0212-x

CrossRef Full Text | Google Scholar

8. Peng H, Long F, Ding C. Feature Selection Based on Mutual Informationcriteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans Pattern Anal (2005) 27:1226–38. doi: 10.1109/TPAMI.2005.159

CrossRef Full Text | Google Scholar

9. Zhang D. Support Vector Machine. In: Fundamentals of Image Data Mining. Springer (2019). p. 179–205. doi: 10.1007/978-3-030-17989-2_8

CrossRef Full Text | Google Scholar

10. Ragab DA, Sharkas M, Marshall S, Ren J. Breast Cancer Detection Using Deep Convolutional Neural Networks and Support Vector Machines. PeerJ (2019) 7:e6201. doi: 10.7717/peerj.6201

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Yassin NIR, Omran S, El Houby EMF, Allam H. Machine Learning Techniques for Breast Cancer Computer Aided Diagnosis Using Different Image Modalities: A Systematic Review. Comput Meth Prog Bio (2018) 156:25–45. doi: 10.1016/j.cmpb.2017.12.012

CrossRef Full Text | Google Scholar

12. DeLong ER, DeLongb DM, Clarke-Pearson DL. Comparing the Areas Under Two or More Correlated Receiver Operating Characteristic Curves: Anonparametric Approach. Biometrics (1988) 44:837–45. doi: 10.2307/2531595

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Moura DC, López MAG. An Evaluation of Image Descriptors Combined With Clinical Data for Breast Cancer Diagnosis. Int J Comput Ass Rad (2013) 8:561–74. doi: 10.1007/s11548-013-0838-2

CrossRef Full Text | Google Scholar

14. Tsochatzidis L, Costaridou L, Pratikakis I. Deep Learning for Breast Cancer Diagnosis From Mammograms—a Comparative Study. J Imaging (2019) 5:37. doi: 10.3390/jimaging5030037

CrossRef Full Text | Google Scholar

15. Kooi T, Litjens G, Van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, et al. Large Scale Deep Learning for Computer Aided Detection of Mammographic Lesions. Med Image Anal (2017) 35:303–12. doi: 10.1016/j.media.2016.07.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: mammography, image feature, deep learning, clinical prediction, radiomics

Citation: Frontiers Production Office (2021) Erratum: Improving the Prediction of Benign or Malignant Breast Masses Using a Combination of Image Biomarkers and Clinical Parameters. Front. Oncol. 11:694094. doi: 10.3389/fonc.2021.694094

Received: 12 April 2021; Accepted: 12 April 2021;
Published: 29 April 2021.

Approved by: Frontiers Editorial Office, Frontiers Media SA, Switzerland

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