Acta Photonica Sinica, Volume. 52, Issue 12, 1210001(2023)
Classification Method of Breast Tissue OCT Images Based on a Double Filtering Residual Network
[1] LIMA S M, KEHM R D, TERRY M B. Global breast cancer incidence and mortality trends by region, age-groups, and fertility patterns[J]. eClinicalMedicine, 38, 100985(2021).
[2] HU K, DING P, WU Y et al. Global patterns and trends in the breast cancer incidence and mortality according to sociodemographic indices: an observational study based on the global burden of diseases[J]. BMJ Open, 9, e028461(2019).
[3] ZHU Wei. Clinical value of breast-conserving surgery for early breast cancer[J]. Henan Journal of Surgery, 27, 82-84.
[4] WILKE L G, CZECHURA T, WANG C et al. Repeat surgery after breast conservation for the treatment of stage 0 to II breast carcinoma: a report from the National Cancer Data Base, 2004-2010[J]. JAMA Surgery, 149, 1296-1305(2014).
[5] JEEVAN R, CROMWELL D A, TRIVELLA M et al. Reoperation rates after breast conserving surgery for breast cancer among women in England: retrospective study of hospital episode statistics[J]. Bmj British Medical Journal, 345, e4505(2012).
[6] HU Jintao, LAI Meina, CHEN Jingwen et al. Application value of frozen pathological section technique in assessing the status of margins in breast-conserving surgery for breast cancer[J]. Medical Innovation of China, 18, 123-127(2021).
[7] LIU Jianying, BU Hong. Pathological evaluation of breast-conserving margin[J]. Chinese Journal of Bases and Clinics in General Surgery, 25, 134-137(2018).
[8] TANG Chaoyi, ZENG Jian. Research progress of breast-conserving surgery for invasive breast cancer[J]. Guangxi Medical Journal, 40, 833-838(2018).
[9] SOUTH F A, CHANEY E J, MARJANOVIC M et al. Differentiation of ex vivo human breast tissue using polarization-sensitive optical coherence tomography[J]. Biomedical Optics Express, 5, 3417-3426(2014).
[10] ERICKSON-BHATT S J, NOLAN R, SHEMONSKI N D et al. In vivo intra-operative breast tumor margin detection using a portable OCT system with a handheld surgical imaging probe[C], 8935, 197-202(2014).
[11] WANG J, XU Y, MESA K J et al. Complementary use of polarization-sensitive and standard OCT metrics for enhanced intraoperative differentiation of breast cancer[J]. Biomedical Optics Express, 9, 6519-6528(2018).
[12] BUTOLA A, PRASAD D K, AHMAD A et al. Deep learning architecture “LightOCT” for diagnostic decision support using optical coherence tomography images of biological samples[J]. Biomedical Optics Express, 11, 5017-5031(2020).
[13] ZHU D, WANG J, MARJANOVIC M et al. Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography[J]. Biomedical Optics Express, 12, 3021-3036(2021).
[14] ZHOU Feiyan, JIN Linpeng, DONG Jun. Review of convolutional neural network[J]. Chinese Journal of Computers, 40, 1229-1251(2017).
[15] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).
[16] GU Y, LU X, YANG L et al. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs[J]. Computers in Biology and Medicine, 103, 220-231(2018).
[17] GLOROT X, BENGIO Y. Understanding the difficulty of training deep feedforward neural networks[C], 249-256(2010).
[18] HE K, ZHANG X, REN S et al. Deep residual learning for image recognition[C], 770-778(2016).
[19] CHEN Y, FAN H, XU B et al. Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution[C], 3435-3444(2019).
[20] WOO S, PARK J, LEE J Y et al. Cbam: Convolutional block attention module[C], 3-19(2018).
[21] LIN M, CHEN Q, YAN S. Network in network[J/OL]. arXiv preprint(2013).
[22] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C], 448-456(2015).
[23] GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[C], 315-323(2011).
[24] HAN J, MORAGA C. The influence of the sigmoid function parameters on the speed of backpropagation learning[C], 195-201(1995).
[26] GU Y, LU X, ZHANG B et al. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography[J]. PLoS One, 14, e0210551(2019).
[27] KHAN S U, ISLAM N, JAN Z et al. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning[J]. Pattern Recognition Letters, 125, 1-6(2019).
[28] SHORTEN C, KHOSHGOFTAAR T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 6, 1-48(2019).
[29] SELVARAJU R R, COGSWELL M, DAS A et al. Grad-cam: visual explanations from deep networks via gradient-based localization[C], 618-626(2017).
[30] RUDER S. An overview of gradient descent optimization algorithms[J/OL]. arXiv preprint(2016).
[31] HUANG G, LIU Z, VAN DER MAATEN L et al. Densely connected convolutional networks[C], 4700-4708(2017).
[32] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J/OL]. arXiv preprint(2014).
[33] TAN M, LE Q. Efficientnet: rethinking model scaling for convolutional neural networks[C], 6105-6114(2019).
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Lihao DING, Zhishan GAO, Dan ZHU, Qun YUAN, Zhenyan GUO. Classification Method of Breast Tissue OCT Images Based on a Double Filtering Residual Network[J]. Acta Photonica Sinica, 2023, 52(12): 1210001
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Received: May. 10, 2023
Accepted: Jul. 26, 2023
Published Online: Feb. 19, 2024
The Author Email: Dan ZHU (danzhu@njust.edu.cn)