Abstract
Ultrasound is a common imaging modality used for targeting suspected cancerous tissue in prostate biopsy. Since ultrasound images have very low specificity and sensitivity for visualizing the cancer foci, a significant body of literature have aimed to develop ultrasound tissue characterization solutions to alleviate this issue. Major challenges are the substantial heterogeneity in data, and the noisy, limited number of labeled data available from pathology of biopsy samples. A recently proposed tissue characterization method uses spectral analysis of time series of ultrasound data taken during the biopsy procedure combined with deep networks. However, the real-value transformations in these networks neglect the phase information of the signal. In this paper, we study the importance of phase information and compare different ways of extracting reliable features including complex neural networks. These networks can help with analyzing the phase information to use the full capacity of the data. Our results show that the phase content can stabilize training specially with non-stationary time series. The proposed approach is generic and can be applied to several other scenarios where the phase information is important and noisy labels are present.
This work is funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Canadian Institutes of Health Research (CIHR).
P. Black, P. Mousavi and P. Abolmaesumi—Joint senior authors.
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Javadi, G. et al. (2020). Complex Cancer Detector: Complex Neural Networks on Non-stationary Time Series for Guiding Systematic Prostate Biopsy. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_50
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