Abstract
We introduce a probabilistic classifier that combines multiple instance learning and relational learning. While multiple instance learning allows automated cancer diagnosis from only image-level annotations, relational learning allows exploiting changes in cell formations due to cancer. Our method extends Gaussian process multiple instance learning with a relational likelihood that brings improved diagnostic performance on two tissue microarray data sets (breast and Barrett’s cancer) when similarity of cell layouts in different tissue regions is used as relational side information.
Chapter PDF
Similar content being viewed by others
Keywords
- Receiver Operating Characteristic Curve
- Local Binary Pattern
- Side Information
- Marginal Likelihood
- Multiple Instance
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS (2003)
Ruifrok, A.C., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23, 291–299 (2001)
Sindhwani, W., et al.: Relational learning with Gaussian processes. In: NIPS (2007)
Xu, Y., et al.: Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering. In: CVPR (2012)
Gelasca, E.D., et al.: Evaluation and benchmark for biological image segmentation. In: ICIP (2008)
Getoor, L., Taskar, B.: Introduction to statistical relational learning. MIT Press (2007)
Kandemir, M., Feuchtinger, A., Walch, A., Hamprecht, F.A.: Digital Pathology: Multiple instance learning can detect Barrett’s cancer. In: ISBI (2014)
Kim, M., Torre, F.: Gaussian processes multiple instance learning. In: ICML (2010)
Maron, O., et al.: A framework for multiple-instance learning. In: NIPS (1998)
Silva, R., Chu, W., Ghahramani, Z.: Hidden common cause relations in relational learning. In: NIPS (2007)
Viola, P., et al.: Multiple instance boosting for object detection. In: NIPS (2005)
Zhang, D., Liu, Y., Si, L., Zhang, J., Lawrence, R.D.: Multiple instance learning on structured data. In: NIPS (2011)
Zhang, G., Yin, J., Li, Z., Su, X., Li, G., Zhang, H.: Automated skin biopsy histopathological image annotation using multi-instance representation and learning. BMC Medical Genomics 6(suppl. 3), S10 (2013)
Zhang, Q., Goldman, S.A.: EM-DD: An improved multiple-instance learning technique. In: NIPS (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kandemir, M., Zhang, C., Hamprecht, F.A. (2014). Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham. https://doi.org/10.1007/978-3-319-10470-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-10470-6_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10469-0
Online ISBN: 978-3-319-10470-6
eBook Packages: Computer ScienceComputer Science (R0)