Supervised Bayesian learning for breast cancer detection in terahertz imaging
Chavez, Tanny, Nagma Vohra, Keith Bailey, Magda El-Shenawee, and Jingxian Wu. “Supervised Bayesian learning for breast cancer detection in terahertz imaging.” Biomedical Signal Processing and Control 70 (2021): 102949.
This paper proposes a supervised multinomial Bayesian learning algorithm for breast cancer detection using terahertz (THz) imaging of freshly excised murine tumors. The proposed algorithm utilizes a multinomial Bayesian probit regression approach, which establishes the link between THz data and classification results by using two different models, a polynomial regression model and a kernel regression model. Such a model-based learning approach employs only a small number of model parameters, thus it requires much less training data when compared with alternative deep learning methods. The training phase of the algorithm is performed by using the histopathology results of formalin-fixed, paraffin embedded (FFPE) samples as ground truth. There is usually a considerable shape mismatch between the freshly excised sample and its FFPE counterpart due to sample dehydration, and such mismatch negatively impacts the quality of the training data. We propose to address this challenge by using an innovative reliability-based training data selection method, where the reliability of the training data is quantified and estimated by using an unsupervised expectation maximization (EM) classification algorithm with soft probabilistic output. Experiment results demonstrate that the proposed multinomial Bayesian probit regression models with reliability-based training data selection achieve better performance than existing methods. Overall, these results demonstrate that the proposed supervised segmentation models represent a promising technique for the region detection with THz imaging of freshly excised breast cancer samples.
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