Abstract
The current gold standard for interpreting patient tissue samples is the visual inspection of whole–slide histopathology images (WSIs) by pathologists. They generate a pathology report describing the main findings relevant for diagnosis and treatment planning. Searching for similar cases through repositories for differential diagnosis is often not done due to a lack of efficient strategies for medical case–based retrieval. A patch–based multimodal retrieval strategy that retrieves similar pathology cases from a large data set fusing both visual and text information is explained in this paper. By fine–tuning a deep convolutional neural network an automatic representation is obtained for the visual content of weakly annotated WSIs (using only a global cancer score and no manual annotations). The pathology text report is embedded into a category vector of the pathology terms also in a non–supervised approach. A publicly available data set of 267 prostate adenocarcinoma cases with their WSIs and corresponding pathology reports was used to train and evaluate each modality of the retrieval method. A MAP (Mean Average Precision) of 0.54 was obtained with the multimodal method in a previously unseen test set. The proposed retrieval system can help in differential diagnosis of tissue samples and during the training of pathologists, exploiting the large amount of pathology data already existing digital hospital repositories.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
http://cancergenome.nih.gov/, as of 11 June 2017.
References
Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medicine-clinical benefits and future directions. Int. J. Med. Inform. 73(1), 1–23 (2004)
Begum, S., Ahmed, M.U., Funk, P., Xiong, N., Folke, M.: Case-based reasoning systems in the health sciences: a survey of recent trends and developments. IEEE Trans. Syst. Man Cybern. 41(4), 421–434 (2011)
Welter, P., Deserno, T.M., Fischer, B., Günther, R.W., Spreckelsen, C.: Towards case-based medical learning in radiological decision making using content-based image retrieval. BMC Med. Inform. Decis. Making 11, 68 (2011)
Jiménez-del-Toro, O.A., Hanbury, A., Langs, G., Foncubierta-Rodríguez, A., Müller, H.: Overview of the VISCERAL retrieval benchmark 2015. In: Müller, H., Jimenez del Toro, O.A., Hanbury, A., Langs, G., Foncubierta Rodriguez, A. (eds.) MRMD 2015. LNCS, vol. 9059, pp. 115–123. Springer, Cham (2015). doi:10.1007/978-3-319-24471-6_10
Caicedo, J.C., Vanegas, J.A., Páez, F., González, F.A.: Histology image search using multimodal fusion. J. Biomed. Inform. 51, 114–128 (2014)
Zhang, X., Liu, W., Dundar, M., Badve, S., Zhang, S.: Towards large-scale histopathological image analysis: hashing-based image retrieval. IEEE Trans. Med. Imaging 34(2), 496–506 (2015)
Kwak, J.T., Hewitt, S.M., Kajdacsy-Balla, A.A., Sinha, S., Bhargava, R.: Automated prostate tissue referencing for cancer detection and diagnosis. BMC Bioinform. 17(1), 227 (2016)
Weinstein, R.S., Graham, A.R., Richter, L.C., Barker, G.P., Krupinski, E.A., Lopez, A.M., Erps, K.A., Bhattacharyya, A.K., Yagi, Y., Gilbertson, J.R.: Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future. Hum. Pathol. 40(8), 1057–1069 (2009)
Doyle, S., Hwang, M., Naik, S., Feldman, M., Tomaszeweski, J., Madabhushi, A.: Using manifold learning for content-based image retrieval of prostate histopathology. In: MICCAI 2007 Workshop on Content-based Image Retrieval for Biomedical Image Archives: Achievements, Problems, and Prospects, pp. 53–62. Citeseer (2007)
Krizhevsky, A., Hinton, G.E.: Using very deep autoencoders for content-based image retrieval. In: ESANN (2011)
Wu, P., Hoi, S.C., Xia, H., Zhao, P., Wang, D., Miao, C.: Online multimodal deep similarity learning with application to image retrieval. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 153–162. ACM (2013)
Wan, J., Wang, D., Hoi, S.C.H., Wu, P., Zhu, J., Zhang, Y., Li, J.: Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 157–166. ACM (2014)
Gutman, D.A., Cobb, J., Somanna, D., Park, Y., Wang, F., Kurc, T., Saltz, J.H., Brat, D.J., Cooper, L.A.D., Kong, J.: Cancer digital slide archive: an informatics resource to support integrated in silico analysis of TCGA pathology data. J. Am. Med. Inform. Assoc. 20(6), 1091–1098 (2013)
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D.M., Forman, D., Bray, F.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–E386 (2015)
Humphrey, P.A.: Gleason grading and prognostic factors in carcinoma of the prostate. Mod. Pathol. 17(3), 292–306 (2004)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Jimenez-del-Toro, O., Atzori, M., Otálora, S., Andersson, M., Eurén, K., Hedlund, M., Rönnquist, P., Müller, H.: Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade gleason score. In: SPIE Medical Imaging. International Society for Optics and Photonics (2017)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, vol. 14, pp. 1188–1196 (2014)
Voorhees, E.M., Ellis, A. (eds.): Proceedings of The Twenty-Fourth Text REtrieval Conference, TREC 2015, Gaithersburg, Maryland, USA, 17–20 November 2015, vol. Special Publication 500–319. National Institute of Standards and Technology (NIST) (2015)
Acknowledgments
This work was partially supported by the Eurostars project E! 9653 SLDESUTO-BOX. The authors would like to thank pathologist Lis Vázquez for her counsel regarding the handling of the pathology reports.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Jimenez-del-Toro, O., Otálora, S., Atzori, M., Müller, H. (2017). Deep Multimodal Case–Based Retrieval for Large Histopathology Datasets. In: Wu, G., Munsell, B., Zhan, Y., Bai, W., Sanroma, G., Coupé, P. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2017. Lecture Notes in Computer Science(), vol 10530. Springer, Cham. https://doi.org/10.1007/978-3-319-67434-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-67434-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67433-9
Online ISBN: 978-3-319-67434-6
eBook Packages: Computer ScienceComputer Science (R0)