Deep Learning-Based Multi-Class Classification of Breast Digital Pathology Images

References

• Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2020.
   Web of Science ®Google Scholar
• Johns LE, Moss SM; Age trial management G. False-positive results in the randomized controlled trial of mammographic screening from age 40 (“age” trial). Cancer Epidemiol Biomark Prev. 2010;19(11):2758–2764. doi:10.1158/1055-9965.EPI-10-0623
   PubMed Web of Science ®Google Scholar
• Nakhleh RE. Error reduction in surgical pathology. Arch Pathol Lab Med. 2006;130(5):630–632. doi:10.5858/2006-130-630-ERISP16683877
   PubMed Web of Science ®Google Scholar
• Raab SS, Grzybicki DM, Janosky JE, et al. Clinical impact and frequency of anatomic pathology errors in cancer diagnoses. Cancer. 2005;104(10):2205–2213. doi:10.1002/cncr.2143116216029
   PubMed Web of Science ®Google Scholar
• Elmore JG, Longton GM, Carney PA, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA. 2015;313(11):1122–1132. doi:10.1001/jama.2015.140525781441
   PubMed Web of Science ®Google Scholar
• Litjens G, Sanchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep. 2016;6(1):1–11. doi:10.1038/srep2628628442746
   PubMed Web of Science ®Google Scholar
• Ghaznavi F, Evans A, Madabhushi A, Feldman M. Digital imaging in pathology: whole-slide imaging and beyond. Annu Rev Pathol. 2013;8(1):331–359. doi:10.1146/annurev-pathol-011811-12090223157334
   PubMed Web of Science ®Google Scholar
• Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: a review. IEEE Rev Biomed Eng. 2009;2:147–171. doi:10.1109/RBME.2009.203486520671804
   PubMedGoogle Scholar
• Gao X, Lin S, Wong TY. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans Biomed Eng. 2015;62(11):2693–2701. doi:10.1109/TBME.2015.244438926080373
   PubMed Web of Science ®Google Scholar
• Ronneberger O, Fischer P, Brox T U-net: convolutional networks for biomedical image segmentation. Paper presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention; 2015.
   Google Scholar
• Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24(10):1559–1567. doi:10.1038/s41591-018-0177-530224757
   PubMed Web of Science ®Google Scholar
• Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061–1068. doi:10.1038/nature0738518772890
   PubMed Web of Science ®Google Scholar
• Weinstein JN, Collisson EA, Mills GB, et al., Cancer Genome Atlas Research Network. The cancer genome atlas pan-Cancer analysis project. Nat Genet. 2013;45(10):1113–1120. doi:10.1038/ng.2764.24071849
   PubMed Web of Science ®Google Scholar
• Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J Mitosis detection in breast cancer histology images with deep neural networks. Paper presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention; 2013.
   Google Scholar
• Wang H, Cruz-Roa A, Basavanhally A, et al. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imag. 2014;1(3):034003. doi:10.1117/1.JMI.1.3.034003
   Google Scholar
• Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S. Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep. 2017;7(1):4172. doi:10.1038/s41598-017-04075-z28646155
   PubMed Web of Science ®Google Scholar
• Spanhol FA, Oliveira LS, Petitjean C, Heutte L. A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng. 2016;63(7):1455–1462. doi:10.1109/TBME.2015.249626426540668
   PubMed Web of Science ®Google Scholar
• Bejnordi BE, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199–2210. doi:10.1001/jama.2017.1458529234806
   PubMed Web of Science ®Google Scholar
• Bejnordi BE, Litjens G, Timofeeva N, et al. Stain specific standardization of whole-slide histopathological images. IEEE Trans Med Imaging. 2015;35(2):404–415. doi:10.1109/TMI.2015.247650926353368
   PubMed Web of Science ®Google Scholar
• Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Paper presented at: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2015.
   Google Scholar
• Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.28778026
   PubMed Web of Science ®Google Scholar
• Cruz-Roa A, Gilmore H, Basavanhally A, et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci Rep. 2017;7(1):46450. doi:10.1038/srep4645028418027
   PubMed Web of Science ®Google Scholar
• Baardwijk AV, Bosmans G, Boersma L, et al. PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes. Int J Radiat Oncol Biol Phys. 2007;68(3):771–778. doi:10.1016/j.ijrobp.2006.12.06717398018
   PubMed Web of Science ®Google Scholar
• Weaver DL, Krag DN, Manna EA, Ashikaga T, Harlow SP, Bauer KD. Comparison of pathologist-detected and automated computer-assisted image analysis detected sentinel lymph node micrometastases in breast cancer. Mod Pathol. 2003;16(11):1159–1163. doi:10.1097/01.MP.0000092952.21794.AD14614056
   PubMed Web of Science ®Google Scholar
• Araujo T, Aresta G, Castro E, et al. Classification of breast cancer histology images using Convolutional Neural Networks. PLoS One. 2017;12(6):e0177544. doi:10.1371/journal.pone.017754428570557
   PubMed Web of Science ®Google Scholar
• Aresta G, Araujo T, Kwok S, et al. BACH: grand challenge on breast cancer histology images. Med Image Anal. 2019;56:122–139. doi:10.1016/j.media.2019.05.01031226662
   PubMed Web of Science ®Google Scholar
• Cao H, Bernard S, Heutte L, Sabourin R Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images. Paper presented at: International Conference Image Analysis and Recognition; 2018.
   Google Scholar
• Rakhlin A, Shvets A, Iglovikov V, Kalinin AA Deep convolutional neural networks for breast cancer histology image analysis. Paper presented at: International Conference Image Analysis and Recognition; 2018.
   Google Scholar
• Wang Z, Dong N, Dai W, Rosario SD, Xing EP Classification of breast cancer histopathological images using convolutional neural networks with hierarchical loss and global pooling. Paper presented at: International Conference Image Analysis and Recognition; 2018.
   Google Scholar
• Chen T, Guestrin C XGBoost: a scalable tree boosting system. Paper presented at: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016.
   Google Scholar
• Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9(1):62–66. doi:10.1109/TSMC.1979.4310076
   Web of Science ®Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90. doi:10.1145/3065386
   Web of Science ®Google Scholar
• Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z Rethinking the inception architecture for computer vision. Paper presented at: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016.
   Google Scholar
• Andre E, Brett K, Roberto AN, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115. doi:10.1038/nature2105628117445
   PubMed Web of Science ®Google Scholar
• Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.1721627898976
   PubMed Web of Science ®Google Scholar
• Abadi M, Barham P, Chen J, et al. TensorFlow: a system for large-scale machine learning. Paper presented at: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation; 2016.
   Google Scholar
• Rosenfield GH, Fitzpatricklins K. A coefficient of agreement as a measure of thematic classification accuracy. Photogramm Eng Remote Sensing. 1986;52(2):223–227.
   Web of Science ®Google Scholar
• Jaafar H. Intra-operative frozen section consultation: concepts, applications and limitations. Malays J Med Sci. 2006;13(1):4.22589584
   PubMedGoogle Scholar
• Lvd M, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(Nov):2579–2605.
   Google Scholar
• Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual Explanations from Deep Networks via Gradient-Based Localization. Int J Comput Vis. 2020;128(2):336–359. doi:10.1007/s11263-019-01228-7
   Web of Science ®Google Scholar
• Kong J, Sertel O, Shimada H, Boyer KL, Saltz JH, Gurcan MN. Computer-aided evaluation of neuroblastoma on whole-slide histology images: classifying grade of neuroblastic differentiation. Pattern Recognit. 2009;42(6):1080–1092. doi:10.1016/j.patcog.2008.10.03528626265
   PubMed Web of Science ®Google Scholar
• Sertel O, Kong J, Shimada H, Catalyurek UV, Saltz JH, Gurcan MN. Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recognit. 2009;42(6):1093–1103. doi:10.1016/j.patcog.2008.08.02720161324
   PubMed Web of Science ®Google Scholar