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Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12446)

Abstract

Deep Convolutional Neural Networks (CNN) are at the backbone of the state–of–the art methods to automatically analyze Whole Slide Images (WSIs) of digital tissue slides. One challenge to train fully-supervised CNN models with WSIs is providing the required amount of costly, manually annotated data. This paper presents a semi-weakly supervised model for classifying prostate cancer tissue. The approach follows a teacher-student learning paradigm that allows combining a small amount of annotated data (tissue microarrays with regions of interest traced by pathologists) with a large amount of weakly-annotated data (whole slide images with labels extracted from the diagnostic reports). The task of the teacher model is to annotate the weakly-annotated images. The student is trained with the pseudo-labeled images annotated by the teacher and fine-tuned with the small amount of strongly annotated data. The evaluation of the methods is in the task of classification of four Gleason patterns and the Gleason score in prostate cancer images. Results show that the teacher-student approach improves significatively the performance of the fully-supervised CNN, both at the Gleason pattern level in tissue microarrays (respectively \(\kappa = 0.594 \pm 0.022\) and \(\kappa = 0.559 \pm 0.034\)) and at the Gleason score level in WSIs (respectively \(\kappa = 0.403 \pm 0.046\) and \(\kappa = 0.273 \pm 0.12\)). Our approach opens the possibility of transforming large weakly–annotated (and unlabeled) datasets into valuable sources of supervision for training robust CNN models in computational pathology.

Keywords

  • Computational pathology
  • Deep learning
  • Semi-weakly supervision
  • Prostate cancer
  • Knowledge distillation

S. Otálora and N. Marini—Equal contribution.

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Notes

  1. 1.

    https://portal.gdc.cancer.gov/projects/TCGA-PRAD Retrieved 1st of July, 2020.

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Acknowledgements.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825292 (ExaMode, https://www.examode.eu). Infrastructure from the SURFsara HPC center was used to train the CNN models in parallel. Otálora thanks Minciencias through the call 756 for Ph.D. studies.

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Otálora, S., Marini, N., Müller, H., Atzori, M. (2020). Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks. In: , et al. Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC MIL3ID LABELS 2020 2020 2020. Lecture Notes in Computer Science(), vol 12446. Springer, Cham. https://doi.org/10.1007/978-3-030-61166-8_21

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  • DOI: https://doi.org/10.1007/978-3-030-61166-8_21

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