Wang, 2009, Multiscale photoacoustic microscopy and computed tomography, Nat. Photonics, 3, 503, 10.1038/nphoton.2009.157
Wang, 2012, Photoacoustic tomography: in vivo imaging from organelles to organs, Science, 335, 1458, 10.1126/science.1216210
Jeon, 2019, Review on practical photoacoustic microscopy, Photoacoustics, 15, 10.1016/j.pacs.2019.100141
Yao, 2013, Photoacoustic microscopy, Laser Photon Rev., 7, 758, 10.1002/lpor.201200060
Kim, 2015, Fast optical-resolution photoacoustic microscopy using a 2-axis water-proofing MEMS scanner, Sci. Rep., 5, 7932, 10.1038/srep07932
Kim, 2016, High-speed and high-SNR photoacoustic microscopy based on a galvanometer mirror in non-conducting liquid, Sci. Rep., 6, 34803, 10.1038/srep34803
Chen, 2020, Wide-field polygon-scanning photoacoustic microscopy of oxygen saturation at 1-MHz A-line rate, Photoacoustics, 20, 10.1016/j.pacs.2020.100195
Wang, 2011, Fast voice-coil scanning optical-resolution photoacoustic microscopy, Opt. Lett., 36, 139, 10.1364/OL.36.000139
Nguyen, 2021, Ultra-widefield photoacoustic microscopy with a dual-channel slider-crank laser-scanning apparatus for in vivo biomedical study, Photoacoustics, 23, 10.1016/j.pacs.2021.100274
Mai, 2021, In vivo quantitative vasculature segmentation and assessment for photodynamic therapy process monitoring using photoacoustic microscopy, Sensors, 21, 1776, 10.3390/s21051776
Maneas, 2020, Photoacoustic imaging of the human placental vasculature, J. Biophotonics, 10.1002/jbio.202070009
Liu, 2020, Single-shot photoacoustic microscopy of hemoglobin concentration, oxygen saturation, and blood flow in sub-microseconds, Photoacoustics, 17, 10.1016/j.pacs.2019.100156
Maslov, 2005, In vivo dark-field reflection-mode photoacoustic microscopy, Opt. Lett., 30, 625, 10.1364/OL.30.000625
Khodaverdi, 2021, Automatic threshold selection algorithm to distinguish a tissue chromophore from the background in photoacoustic imaging, Biomed. Opt. Express, 12, 3836, 10.1364/BOE.422170
Baik, 2020, Super wide-field photoacoustic microscopy of animals and humans in vivo, IEEE Trans. Med. Imaging, 39, 975, 10.1109/TMI.2019.2938518
B. Yilmaz, A. Ba, E. Jasiuniene, H.K. Bui, G. Berthiau, Comparison of different non-destructive testing techniques for bonding quality evaluation, in: 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace), IEEE, 2019, pp. 92–97.
Zhang, 2009, Automatic algorithm for skin profile detection in photoacoustic microscopy, J. Biomed. Opt., 14, 10.1117/1.3122362
Yang, 2021, Review of deep learning for photoacoustic imaging, Photoacoustics, 21, 10.1016/j.pacs.2020.100215
Deng, 2021, Deep learning in photoacoustic imaging: a review, J. Biomed. Opt., 10.1117/1.JBO.26.4.040901
Grohl, 2021, Deep learning for biomedical photoacoustic imaging: a review, Photoacoustics, 22, 10.1016/j.pacs.2021.100241
Lan, 2019, Reconstruct the photoacoustic image based on deep learning with multi-frequency ring-shape transducer array, Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2019, 7115
Gutta, 2017, Deep neural network-based bandwidth enhancement of photoacoustic data, J. Biomed. Opt., 22, 10.1117/1.JBO.22.11.116001
Li, 2020, NETT: solving inverse problems with deep neural networks, Inverse Probl., 36, 10.1088/1361-6420/ab6d57
DiSpirito, 2021, Reconstructing undersampled photoacoustic microscopy images using deep learning, IEEE Trans. Med. Imaging, 40, 562, 10.1109/TMI.2020.3031541
Hauptmann, 2018, Model-based learning for accelerated, limited-view 3-D photoacoustic tomography, IEEE Trans. Med. Imaging, 37, 1382, 10.1109/TMI.2018.2820382
Zhang, 2019, Photoacoustic image classification and segmentation of breast cancer: a feasibility study, IEEE Access, 7, 5457, 10.1109/ACCESS.2018.2888910
Cai, 2018, End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging, Opt. Lett., 43, 2752, 10.1364/OL.43.002752
Jnawali, 2019, Transfer learning for automatic cancer tissue detection using multispectral photoacoustic imaging, Medical Imaging 2019: Computer-Aided Diagnosis, Int. Soc. Opt. Photonics
Boink, 2020, Algorithm for joint photo-acoustic reconstruction and segmentation, IEEE Trans. Med. Imaging, 39, 129, 10.1109/TMI.2019.2922026
Chlis, 2020, A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography, Photoacoustics, 20, 10.1016/j.pacs.2020.100203
Lafci, 2021, Deep learning for automatic segmentation of hybrid optoacoustic ultrasound (OPUS) images, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 68, 688, 10.1109/TUFFC.2020.3022324
Ma, 2020, Human breast numerical model generation based on deep learning for photoacoustic imaging, Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2020, 1919
Lafci, 2020, Efficient segmentation of multi-modal optoacoustic and ultrasound images using convolutional neural networks, Photons Plus Ultrasound: Imaging and Sensing 2020, Int. Soc. Opt. Photonics
O. Ronneberger, P. Fischer, T. Brox, U-net: convolutional networks for biomedical image segmentation, in: International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, 2015, pp. 234–241.
Badrinarayanan, 2017, Segnet: a deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39, 2481, 10.1109/TPAMI.2016.2644615
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431–3440.
Zabinski, 2017, 130
G. Chen, P. Chen, Y. Shi, C.-Y. Hsieh, B. Liao, S. Zhang, Rethinking the usage of batch normalization and dropout in the training of deep neural networks, 2019, p. arXiv:1905.05928.
Lee, 2020, Revisiting spatial dropout for regularizing convolutional neural networks, Multimed. Tools Appl., 79, 34195, 10.1007/s11042-020-09054-7
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014, p. arXiv:1409.1556.
Z. Zheng, Z. Li, A. Nagar, W. Kang, Compact deep convolutional neural networks for image classification, in: Proc. ICMEW, 2015, pp. 1–6.
Maksoud, 2019, Medical images analysis based on multilabel classification, 209
W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao, Y. Rui, Visualizing and comparing AlexNet and VGG using deconvolutional layers, in: Proceedings of the 33 rd International Conference on Machine Learning, 2016.
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 1097
Szeliski, 2010
Shorten, 2019, A survey on Image Data Augmentation for Deep Learning, J. Big Data, 6, 1, 10.1186/s40537-019-0197-0
Sharma, 2017, Activation functions in neural networks, Towards Data Sci., 310
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, TensorFlow: large-scale machine learning on heterogeneous distributed systems, 2016, p. arXiv:1603.04467.
D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, 2014, p. arXiv:1412.6980.
Costa, 2019, Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images, BMC Med. Imaging, 19, 85, 10.1186/s12880-019-0389-2