Abstract
Patch-based image classification approaches are frequently employed in digital pathology applications with the goal to automatically delineate diagnostically important regions in digital slides. However, patches into which a slide is partitioned are often classified singly and sequentially without additional context. To address this issue, we tested a contextual classification of image patches with soft voting applied to a multi-class classification problem. The context comprised five or nine overlapping patches. The classification is performed using convolutional neural networks (CNNs) trained to recognize four histologically distinct growth patterns of lung adenocarcioma and non-tumor areas. Classification with soft voting outperformed sequential classification of patches yielding higher whole slide classification accuracy. The F1-scores for the four tumor growth patterns improved by 3% and 4.9% when the context consisted of five and nine neighboring patches, respectively. We conclude that the context can improve classification performance of areas sharing the same histological features. Soft voting is a non-trainable approach and therefore straightforward to implement. However, it is computationally more expensive than the classical single patch-based approach.
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References
Awan, R., Koohbanani, N.A., Shaban, M., Lisowska, A., Rajpoot, N.: Context-aware learning using transferable features for classification of breast cancer histology images. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) Image Analysis and Recognition, pp. 788–795. Springer International Publishing, Cham (2018)
Bejnordi, E., Miko, V., van Diest, P.J., et al: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 (2017)
Coudray, N., Ocampo, P.S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A.L., Razavian, N., Tsirigos, A.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nature Med. 24(10), 1559 (2018)
Zanjani, F.G., Zinger, S.: Cancer detection in histopathology whole-slide images using conditional random fields on deep embedded spaces (2018)
Gecer, B., Aksoy, S., Mercan, E., Shapiro, L.G., Weaver, D.L., Elmore, J.G.: Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recog. 84, 345–356 (2018)
Gertych, A., Swiderska-Chadaj, Z., Ma, Z., Ing, N., Markiewicz, T., Cierniak, S., Salemi, H., Guzman, S., Walts, A.E., Knudsen, B.S.: Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci. Rep. 9, 1483 (2019)
Goode, A., Gilbert, B., Harkes, J., Jukic, D., Satyanarayanan, M.: OpenSlide: a vendor-neutral software foundation for digital pathology. J. Pathol. Inf. 4(1), 27 (2013)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hou, L., Samaras, D., Kur, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2424–2433 (2016)
Ing, N., Salman, S., Ma, Z., Walts, A., Knudsen, B., Gertych, A.: Machine learning can reliably distinguish histological patterns of micropapillary and solid lung adenocarcinomas. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) 2016 Proceedings of 5th International Conference on Information Technologies in Medicine, ITIB 2016 Kamien Slaski, Poland, 20–22 June 2016, vol. 2, pp. 193–206. Springer International Publishing, Cham (2016)
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inf. 7 (2016)
Kadota, K., Yeh, Y.C., Sima, C.S., Rusch, V.W., Moreira, A.L., Adusumilli, P.S., Travis, W.D.: The cribriform pattern identifies a subset of acinar predominant tumors with poor prognosis in patients with stage I lung adenocarcinoma: a conceptual proposal to classify cribriform predominant tumors as a distinct histologic subtype. Modern Pathol. 27(5), 690 (2014)
Li,W., Li, J., Sarma, K.V., Ho, K.C., Shen, S., Knudsen, B.S., Gertych, A., Arnold, C.W.: Path R-CNN for prostate cancer diagnosis and gleason grading of histological images. IEEE Trans. Med. Imaging 38(4), 945–954 (2019)
Ma, Z., Swiderska-Chadaj, Z., Ing, N., Salemi, H., McGovern, D., Knudsen, B., Gertych, A.: Semantic segmentation of colon glands in inflammatory bowel disease biopsies. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) Proceedings 6th International Conference, ITIB’2018, Kamien Slaski, Poland, June 18-20, 2018, Information Technology in Biomedicine, pp. 379–392. Springer International Publishing, Cham (2019)
Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D.A., Barnholtz-Sloan, J.S., Velázquez Vega, J.E., Brat, D.J., Cooper, L.A.D.: Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl Acad. Sci. 115(13), E2970–E2979 (2018)
Russell, P.A., Wainer, Z., Wright, G.M., Daniels, M., Conron, M., Williams, R.A.: Does lung adenocarcinoma subtype predict patient survival? A clinicopathologic study based on the new international association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary lung adenocarcinoma classification. J. Thorac. Oncol. 6(9), 1496–1504 (2011)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., Litjens, G., van der Laak, J., Ciompi, F.: Whole-slide mitosis detection in H&E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks. IEEE Trans. Med. Imaging 37(9), 2126–2136 (2018)
Thunnissen, E., Beasley, M.B., Borczuk, A.C., Brambilla, E., Chirieac, L.R., Dacic, S., Flieder, D., Gazdar, A., Geisinger, K., Hasleton, P., et al.: Reproducibility of histopathological subtypes and invasion in pulmonary adenocarcinoma. An international interobserver study. Mod. Pathol. 25(12), 1574 (2012)
Tsao, M.S., Marguet, S., Le Teuff, G., Lantuejoul, S., Shepherd, F.A., Seymour, L., Kratzke, R., Graziano, S.L., Popper, H.H., Rosell, R., et al.: Subtype classification of lung adenocarcinoma predicts benefit from adjuvant chemotherapy in patients undergoing complete resection. J. Clin. Oncol. 33(30), 3439 (2015)
Acknowledgments
This work has been supported by in part by the Precision Health Grant at C-S, seed grants from the department of Surgery at Cedars-Sinai Medical Center and a grant from the National Science Centre, Poland (grant 2016/23/N/ST6/02076).
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Swiderska-Chadaj, Z. et al. (2019). Contextual Classification of Tumor Growth Patterns in Digital Histology Slides. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2019. Advances in Intelligent Systems and Computing, vol 1011. Springer, Cham. https://doi.org/10.1007/978-3-030-23762-2_2
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DOI: https://doi.org/10.1007/978-3-030-23762-2_2
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