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1 April 2020 Automated surgical margin assessment in breast conserving surgery using SFDI with ensembles of self-confident deep convolutional networks
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Abstract
With an adequate tissue dataset, supervised classification of tissue optical properties can be achieved in SFDI images of breast cancer lumpectomies with deep convolutional networks. Nevertheless, the use of a black-box classifier in current ex vivo setups provides output diagnostic images that are inevitably bound to show misclassified areas due to inter- and intra-patient variability that could potentially be misinterpreted in a real clinical setting. This work proposes the use of a novel architecture, the self-introspective classifier, where part of the model is dedicated to estimating its own expected classification error. The model can be used to generate metrics of self-confidence for a given classification problem, which can then be employed to show how much the network is familiar with the new incoming data. A heterogenous ensemble of four deep convolutional models with self-confidence, each sensitive to a different spatial scale of features, is tested on a cohort of 70 specimens, achieving a global leave-one-out cross-validation accuracy of up to 81%, while being able to explain where in the output classification image the system is most confident.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arturo Pardo, José A. Gutiérrez-Gutiérrez, Samuel S. Streeter, Benjamin W. Maloney, José M. López-Higuera, Brian W. Pogue, and Olga M. Conde "Automated surgical margin assessment in breast conserving surgery using SFDI with ensembles of self-confident deep convolutional networks", Proc. SPIE 11362, Clinical Biophotonics, 113620I (1 April 2020); https://doi.org/10.1117/12.2554965
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