Fluorescence lifetime imaging endomicroscopy-based ex-vivo lung cancer prediction using multi-scale concatenated-dilation convolutional neural networks

Qiang Wang; James R. Hopgood; Marta Vallejo

doi:10.1117/12.2580467

15 February 2021 Fluorescence lifetime imaging endomicroscopy-based ex-vivo lung cancer prediction using multi-scale concatenated-dilation convolutional neural networks

Qiang Wang, James R. Hopgood, Marta Vallejo

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115972O (2021) https://doi.org/10.1117/12.2580467
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Deep learning technologies have been successfully applied to automatic diagnostics of ex-vivo lung cancer with fluorescence lifetime imaging endomicroscopy (FLIM). Recent advance in convolutional neural networks (CNNs) by splitting input features for multi-scale feature extraction as a feature-level aggregation, has achieved further improvement in visual recognition. However, due to the splitting, correlations among input features are no longer retained. To exploit the advantages of hierarchical multi-scale architectures, while maintaining the correlations as global information, we propose a novel architecture, namely multi-scale concatenated-dilation (MSCD) at a layer level. The MSCD performs multi-scale feature extraction on input features without the splitting. In addition, we substitute the Addition aggregation in the original hierarchical architecture with the Concatenation to retrieve more features. At the same time, we also introduce dilated convolutions to replace the linear convolutions to further enlarge the receptive field. We evaluate the performance of MSCD by integrating it into ResNet, on over 60,000 FLIM images collected from 14 patients, using a custom fiber-based FLIM system, with various user-specified configurations. Accuracy, precision, recall, and the area under the receiver operating characteristic curve are used as the metrics. We first demonstrate the superiority of our MSCD model over the backbone ResNet and other state-of-the-art CNNs in terms of higher scores with lower complexity over the metrics. Moreover, we empirically demonstrate the superiority of the Concatenation aggregation over the Addition on convolution and scale efficiency. Furthermore, we compare the MSCD with Res2Net to illustrate the advantages and disadvantages of feature-/layer-level multi-scale aggregation.

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Citation Download Citation

Qiang Wang, James R. Hopgood, and Marta Vallejo "Fluorescence lifetime imaging endomicroscopy-based ex-vivo lung cancer prediction using multi-scale concatenated-dilation convolutional neural networks", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115972O (15 February 2021); https://doi.org/10.1117/12.2580467

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