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Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Ultrasound imaging is routinely used in prostate biopsy, which involves obtaining prostate tissue samples using a systematic, yet, non-targeted approach. This approach is blinded to individual patient intraprostatic pathology, and unfortunately, has a high rate of false negatives.

Methods

In this paper, we propose a deep network for improved detection of prostate cancer in systematic biopsy. We address several challenges associated with training such network: (1) Statistical labels: Since biopsy core’s pathology report only represents a statistical distribution of cancer within the core, we use multiple instance learning (MIL) networks to enable learning from ultrasound image regions associated with those data; (2) Limited labels: The number of biopsy cores are limited to at most 12 per patient. As a result, the number of samples available for training a deep network is limited. We alleviate this issue by effectively combining Independent Conditional Variational Auto Encoders (ICVAE) with MIL. We train ICVAE to learn label-invariant features of RF data, which is subsequently used to generate synthetic data for improved training of the MIL network.

Results

Our in vivo study includes data from 339 prostate biopsy cores of 70 patients. We achieve an area under the curve, sensitivity, specificity, and balanced accuracy of 0.68, 0.77, 0.55 and 0.66, respectively.

Conclusion

The proposed approach is generic and can be applied to several other scenarios where unlabeled data and noisy labels in training samples are present.

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Author information

Authors and Affiliations

  1. The University of British Columbia, Vancouver, BC, Canada

    Golara Javadi, Samareh Samadi, Sharareh Bayat, Mehran Pesteie, Mohammad H. Jafari, Samira Sojoudi & Purang Abolmaesumi

  2. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  3. Vancouver General Hospital, Vancouver, BC, Canada

    Claudia Kesch, Antonio Hurtado, Silvia Chang & Peter Black

Authors
  1. Golara Javadi
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  2. Samareh Samadi
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  3. Sharareh Bayat
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  4. Mehran Pesteie
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  5. Mohammad H. Jafari
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  6. Samira Sojoudi
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  9. Silvia Chang
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  10. Parvin Mousavi
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  12. Purang Abolmaesumi
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Corresponding author

Correspondence to Golara Javadi.

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All authors confirm that there are no known conflicts of interest with this publication.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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This work is funded in part by the Canadian Institutes of Health Research (CIHR) and in part by the Natural Sciences and Engineering Research Council of Canada (NSERC)

Golara Javadi and Samareh Samadi are Joint first authors. Parvin Mousavi, Peter Black and Purang Abolmaesumi are Joint senior authors.

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Javadi, G., Samadi, S., Bayat, S. et al. Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study. Int J CARS 15, 1023–1031 (2020). https://doi.org/10.1007/s11548-020-02168-1

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  • DOI: https://doi.org/10.1007/s11548-020-02168-1

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