Abstract
Pathologists are adopting whole slide images (WSIs) for diagnostic purposes. While doing so, pathologists should have all the information needed to make best diagnoses rapidly, while supervising computational pathology tools in real-time. Computational pathology has great potential for augmenting pathologists’ accuracy and efficiency, but concern exists regarding trust for ‘black-box AI’ solutions. Explainable AI (xAI) can reveal underlying reasons for its results, to promote safety, reliability, and accountability for critical tasks such as pathology diagnosis. Built on a hierarchy of computational and traditional image analysis algorithms, we present the development of our proprietary xAI software platform, HistoMapr, for pathologists to improve their efficiency and accuracy when viewing WSIs. HistoMapr and xAI represent a powerful and transparent alternative to ‘black-box’ AI. HistoMapr previews WSIs then presents key diagnostic areas first in an interactive, explainable fashion. Pathologists can access xAI features via a “Why?” button in the interface. Furthermore, two critical early application examples are presented: 1) Intelligent triaging that involves xAI estimation of difficulty for new cases to be forwarded to subspecialists or generalist pathologists; 2) Retrospective quality assurance entails detection of potential discrepancies between finalized results and xAI reviews. Finally, a prototype is presented for atypical ductal hyperplasia, a diagnostic challenge in breast pathology, where xAI descriptions were based on computational pipeline image results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Food and Drug Administration, U.S.A.: Intellisite3 pathology solution (pips, Philips medical systems) (2017)
Food and Drug Administration, U.S.A.: Aperio AT2 DX system (2019)
Pantanowitz, L., Sharma, A., Carter, A.B., Kurc, T., Sussman, A., Saltz, J.: Twenty years of digital pathology: an overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives. J. Pathol. Inf. 9 (2018, online)
Louis, D.N., et al.: Computational pathology: a path ahead. Arch. Pathol. Lab. Med. 140(1), 41–50 (2016)
Fuchs, T.J., Buhmann, J.M.: Computational pathology: challenges and promises for tissue analysis. Comput. Med. Imaging Graph. 35(7–8), 515–530 (2011)
Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550–1560 (2017)
Eisses, J.F., et al.: A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis. PloS One 9(10) (2014, online)
Mercan, E., Mehta, S., Bartlett, J., Shapiro, L.G., Weaver, D.L., Elmore, J.G.: Assessment of machine learning of breast pathology structures for automated differentiation of breast cancer and high-risk proliferative lesions. JAMA Netw. Open 2(8), e198777 (2019)
Tosun, A.B., Sokmensuer, C., Gunduz-Demir, C.: Unsupervised tissue image segmentation through object-oriented texture. In: 2010 20th International Conference on Pattern Recognition, pp. 2516–2519. IEEE (2010)
Li, H., Whitney, J., Bera, K., Gilmore, H., Thorat, M.A., Badve, S., Madabhushi, A.: Quantitative nuclear histomorphometric features are predictive of oncotype DX risk categories in ductal carcinoma in situ: preliminary findings. Breast Cancer Res. 21(1), 114 (2019)
Huang, H., et al.: Cancer diagnosis by nuclear morphometry using spatial information. Pattern Recogn. Lett. 42, 115–121 (2014)
Dong, F., et al.: Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PloS One 9(12) (2014, online)
Nawaz, S., Yuan, Y.: Computational pathology: exploring the spatial dimension of tumor ecology. Cancer Lett. 380(1), 296–303 (2016)
Fuchs, T.J., Wild, P.J., Moch, H., Buhmann, J.M.: Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5242, pp. 1–8. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85990-1_1
Tosun, A.B., Yergiyev, O., Kolouri, S., Silverman, J.F., Rohde, G.K.: Detection of malignant mesothelioma using nuclear structure of mesothelial cells in effusion cytology specimens. Cytometry Part A 87(4), 326–333 (2015)
Farahani, N., Liu, Z., Jutt, D., Fine, J.L.: Pathologists’ computer-assisted diagnosis: a mock-up of a prototype information system to facilitate automation of pathology sign-out. Arch. Pathol. Lab. Med. 141(10), 1413–1420 (2017)
Fine, J.L.: 21st century workflow: a proposal. J. Pathol. Inf. 5 (2014, online)
Tosun, A.B., et al.: Histological detection of high-risk benign breast lesions from whole slide images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 144–152. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_17
Li, C., Wang, X., Liu, W., Latecki, L.J.: DeepMitosis: mitosis detection via deep detection, verification and segmentation networks. Med. Image Anal. 45, 121–133 (2018)
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inf. 7 (2016, online)
Aresta, G., et al.: BACH: grand challenge on breast cancer histology images. Med. Image Anal. 56, 122–139 (2019)
Liu, Y., Gadepalli, K., et al.: Detecting cancer metastases on gigapixel pathology images. arXiv preprint arXiv:1703.02442 (2017)
Bejnordi, B.E., et al.: Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. J. Med. Imaging (Bellingham) 4(4), 044504 (2017)
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019)
Gunning, D.: Explainable artificial intelligence (xAI). Defense Advanced Research Projects Agency (DARPA), nd Web 2 (2017)
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z.: XAI–explainable artificial intelligence. Sci. Robot. 4(37) (2019, online)
Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Metrics for explainable AI: challenges and prospects. arXiv preprint arXiv:1812.04608 (2018)
Samek, W., Wiegand, T., Müller, K.R.: Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296 (2017)
Uttam, S., et al.: Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks. bioRxiv (2019)
USCAP: United States and Canadian academy of pathology (USCAP) annual meeting
DPA: Pathology visions conference
Elmore, J.G., et al.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11), 1122–1132 (2015)
Montalto, M.C.: An industry perspective: an update on the adoption of whole slide imaging. J. Pathol. Inf. 7 (2016, online)
Jones, T., Nguyen, L., Torun, A.B., Chennubhotla, S., Fine, J.L.: Computational pathology versus manual microscopy: comparison based on workflow simulations of breast core biopsies. In: Laboratory Investigation, vol. 97, Nature Publishing Group 75 Varick St, 9th Flr, New York, NY, 10013-1917 USA, pp. 398A–398A (2017)
Simpson, J.F., Boulos, F.I.: Differential diagnosis of proliferative breast lesions. Surg. Pathol. Clin. 2(2), 235–246 (2009)
Onega, T., et al.: The diagnostic challenge of low-grade ductal carcinoma in situ. Eur. J. Cancer 80, 39–47 (2017)
Nguyen, L., Tosun, A.B., Fine, J.L., Taylor, D.L., Chennubhotla, S.C.: Architectural patterns for differential diagnosis of proliferative breast lesions from histopathological images. In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 152–155. IEEE (2017)
Nguyen, L., Tosun, A.B., Fine, J.L., Lee, A.V., Taylor, D.L., Chennubhotla, S.C.: Spatial statistics for segmenting histological structures in H&E stained tissue images. IEEE Trans. Med. Imaging 36(7), 1522–1532 (2017)
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 427–436 (2015)
Tizhoosh, H.R., Pantanowitz, L.: Artificial intelligence and digital pathology: challenges and opportunities. J. Pathol. Inf. 9 (2018, online)
Hudec, M., Bednárová, E., Holzinger, A.: Augmenting statistical data dissemination by short quantified sentences of natural language. J. Off. Stat. 34(4), 981–1010 (2018)
European Commission: Ethics guidelines for trustworthy AI (European commission, 2019) (2019)
US: The white house, executive office of the president of the United States, national artificial intelligence research and development strategic plan (2019)
Holzinger, A., Biemann, C., Pattichis, C.S., Kell, D.B.: What do we need to build explainable AI systems for the medical domain? arXiv preprint arXiv:1712.09923 (2017)
Floridi, L.: Establishing the rules for building trustworthy AI. Nat. Mach. Intell. 1(6), 261–262 (2019)
Evans, A.J., et al.: Us food and drug administration approval of whole slide imaging for primary diagnosis: a key milestone is reached and new questions are raised. Arch. Pathol. Lab. Med. 142(11), 1383–1387 (2018)
Ribeiro, M.T., Singh, S., Guestrin, C.: Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386 (2016)
Miller, T., Howe, P., Sonenberg, L.: Explainable AI: beware of inmates running the asylum or: how i learnt to stop worrying and love the social and behavioural sciences. arXiv preprint arXiv:1712.00547 (2017)
Montavon, G., Samek, W., Müller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Proc. 73, 1–15 (2018)
Core, M.G., Lane, H.C., Van Lent, M., Gomboc, D., Solomon, S., Rosenberg, M.: Building explainable artificial intelligence systems. In: AAAI, pp. 1766–1773 (2006)
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
Holzinger, A., Carrington, A., Müller, H.: Measuring the quality of explanations: the system causability scale (SCS). KI-Künstliche Intell. 34(2), 193–198 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Tosun, A.B., Pullara, F., Becich, M.J., Taylor, D.L., Chennubhotla, S.C., Fine, J.L. (2020). HistoMapr™: An Explainable AI (xAI) Platform for Computational Pathology Solutions. In: Holzinger, A., Goebel, R., Mengel, M., Müller, H. (eds) Artificial Intelligence and Machine Learning for Digital Pathology. Lecture Notes in Computer Science(), vol 12090. Springer, Cham. https://doi.org/10.1007/978-3-030-50402-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-50402-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50401-4
Online ISBN: 978-3-030-50402-1
eBook Packages: Computer ScienceComputer Science (R0)