Dual-attention EfficientNet based on multi-view feature fusion for cervical squamous intraepithelial lesions diagnosis

Sung, 2021, Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin, 71, 209, 10.3322/caac.21660 WHO, 2014, Human papillomavirus and related cancers in world Chen T, Ma X, Liu X, Wang W, Feng R, Chen J, et al. Multi-view learning with feature level fusion for cervical dysplasia diagnosis. Lect Notes Comput Sci (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11764 LNCS, 2019, p. 329–38. 10.1007/978-3-030-32239-7_37. Xu T, Zhang H, Huang X, Zhang S, Metaxas DN. Multimodal deep learning for cervical dysplasia diagnosis. Lect Notes Comput Sci (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9901 LNCS, 2016, p. 115–23. 10.1007/978-3-319-46723-8_14. Denkçeken, 2013, Elastic light single-scattering spectroscopy for the detection of cervical precancerous ex vivo, IEEE Trans Biomed Eng, 60, 123, 10.1109/TBME.2012.2225429 Mustafa, 2020, A narrative review: Classification of pap smear cell image for cervical cancer diagnosis, Oncologie, 22, 53, 10.32604/oncologie.2020.013660 Yang, 2021, Feature fusion combined with tissue Raman spectroscopy to screen cervical cancer, J Raman Spectrosc, 52, 1830, 10.1002/jrs.6246 Cheng, 2021, Robust whole slide image analysis for cervical cancer screening using deep learning, Nat Commun, 12, 1, 10.1038/s41467-021-25296-x Guo, 2016, Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification, IEEE J Biomed Heal Informatics, 20, 1595, 10.1109/JBHI.2015.2483318 Greenspan, 2009, Automatic detection of anatomical landmarks in uterine cervix images, IEEE Trans Med Imaging, 28, 454, 10.1109/TMI.2008.2007823 Aziz Mohamad, 2015, Visual inspection after acetic acid (via) as an alternative screening tool for cancer cervix, Gynecol Obstet, 5, 204, 10.4172/2161-0932.1000336 Huchko, 2015, A randomized trial comparing the diagnostic accuracy of visual inspection with acetic acid to visual inspection with Lugol’s iodine for cervical cancer screening in HIV infected women, PLoS ONE, 10, 10.1371/journal.pone.0118568 Massad, 2009, The accuracy of colposcopic grading for detection of high-grade cervical intraepithelial neoplasia, J Low Genit Tract Dis, 13, 137, 10.1097/LGT.0b013e31819308d4 Massad, 2008, Interobserver agreement in the assessment of components of colposcopic grading, Obstet Gynecol, 111, 1279, 10.1097/AOG.0b013e31816baed1 Jusman, 2014, Intelligent screening systems for cervical cancer, Sci World J, 2014, 10.1155/2014/810368 Sato, 2018, Application of deep learning to the classification of images from colposcopy, Oncol Lett, 15, 3518 Woo S, Park J, Lee JY, Kweon IS. CBAM: Convolutional block attention module. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics), vol. 11211 LNCS, 2018, p. 3–19. 10.1007/978-3-030-01234-2_1. Hou, 2021, Coordinate attention for efficient mobile network design, Proc IEEE/CVF Conf Comput Vis Pattern Recognit, 13713 Ma, 2021, Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer, J Appl Clin Med Phys Rigaud, 2021, Automatic segmentation using deep learning to enable online dose optimization during adaptive radiation therapy of cervical cancer, Int J Radiat Oncol Biol Phys, 109, 1096, 10.1016/j.ijrobp.2020.10.038 Liu Z, Chen W, Guan H, Zhen H, Shen J, Liu X, et al. An adversarial deep-learning-based model for cervical cancer CTV segmentation with multicenter blinded randomized controlled validation. Front Oncol 2021:3223. Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Anal, 42, 60, 10.1016/j.media.2017.07.005 Xu T, Xin C, Long LR, Antani S, Xue Z, Kim E, et al. A new image data set and benchmark for cervical dysplasia classification evaluation. Lect Notes Comput Sci (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9352, 2015, p. 26–35. 10.1007/978-3-319-24888-2_4. Ji, 2000, Classifying cervix tissue patterns with texture analysis, Pattern Recognit, 33, 1561, 10.1016/S0031-3203(99)00123-5 Xu T, Kim E, Huang X. Adjustable adaboost classifier and pyramid features for image-based cervical cancer diagnosis. Proc. - Int. Symp. Biomed. Imaging, vol. 2015- July, 2015, p. 281–5. 10.1109/ISBI.2015.7163868. Acosta-Mesa, 2009, Aceto-white temporal pattern classification using k-NN to identify precancerous cervical lesion in colposcopic images, Comput Biol Med, 39, 778, 10.1016/j.compbiomed.2009.06.006 Park, 2011, Domain-specific image analysis for cervical neoplasia detection based on conditional random fields, IEEE Trans Med Imaging, 30, 867, 10.1109/TMI.2011.2106796 Devi, 2016, Classification of cervical cancer using artificial neural networks, Procedia Comput Sci, 89, 465, 10.1016/j.procs.2016.06.105 Catarino, 2018, Accuracy of combinations of visual inspection using acetic acid or lugol iodine to detect cervical precancer: a meta-analysis, BJOG Int J Obstet Gynaecol, 125, 545, 10.1111/1471-0528.14783 Itti, 1998, A model of saliency-based visual attention for rapid scene analysis, IEEE Trans Pattern Anal Mach Intell, 20, 1254, 10.1109/34.730558 Liu, 2018, Skeleton-based human action recognition with global context-aware attention LSTM networks, IEEE Trans Image Process, 27, 1586, 10.1109/TIP.2017.2785279 Chen, 2019, Three-stream attention-aware network for RGB-D salient object detection, IEEE Trans Image Process, 28, 2825, 10.1109/TIP.2019.2891104 Hu, 2020, An efficient convolutional neural network model based on object-level attention mechanism for casting defect detection on radiography images, IEEE Trans Ind Electron, 67, 10922, 10.1109/TIE.2019.2962437 Hu, 2018, Squeeze-and-excitation networks, Proc IEEE Conf Comput Vis pattern Recognit, 7132 Erhan, 2009, Visualizing higher-layer features of a deep network, Bernoulli, 1341, 1 Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016- Decem, 2016, p. 2921–9. 10.1109/CVPR.2016.319. Selvaraju, 2020, Grad-CAM: visual explanations from deep networks via gradient-based localization, Int J Comput Vis, 128, 336, 10.1007/s11263-019-01228-7 Tan M, Le Q V. EfficientNet: Rethinking model scaling for convolutional neural networks. In: 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019- June, 2019, p. 10691–700. Zagoruyko, 2017, Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer Müller, 2019, When does label smoothing help?, Adv Neural Inf Process Syst, 32 Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016- Decem, 2016, p. 2818–26. 10.1109/CVPR.2016.308. Pang T, Xu K, Dong Y, Du C, Chen N, Zhu J. Rethinking Softmax cross-entropy loss for adversarial robustness. ArXiv Prepr ArXiv190510626 2019. Taylor, 2009, Transfer learning for reinforcement learning domains: A survey, J Mach Learn Res, 10, 1633 Bottou L. Stochastic gradient descent tricks. Lect Notes Comput Sci (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7700 LECTU, Springer; 2012, p. 421–36. 10.1007/978-3-642-35289-8_25. Loshchilov I, Hutter F. SGDR: Stochastic gradient descent with warm restarts. 5th Int Conf Learn Represent ICLR - Conf Track Proc 2017. Guo Z, Li X, Huang H, Quo N, Li Q. Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes. In: Proc. - Int. Symp. Biomed. Imaging, vol. 2018- April, 2018, p. 903–7. 10.1109/ISBI.2018.8363717. Ursuleanu, 2021, The Use of Artificial intelligence on segmental volumes, constructed from MRI and CT images, in the diagnosis and staging of cervical cancers and thyroid cancers: a study protocol for a randomized controlled trial, J Biomed Sci Eng, 14, 300, 10.4236/jbise.2021.146025 Chandran, 2021, Diagnosis of cervical cancer based on ensemble deep learning network using colposcopy images, Biomed Res Int, 10.1155/2021/5584004 Xue, 2020, The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence, BMC Med, 18, 10.1186/s12916-020-01613-x Zhao, 2022, The performance of artificial intelligence in cervical colposcopy: a retrospective data analysis, J Oncol, 2022, 4370851, 10.1155/2022/4370851 Cho, 2020, Classification of cervical neoplasms on colposcopic photography using deep learning, Sci Rep, 10 Park, 2021, Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images, Sci Rep, 11, 1 Peng, 2021, Diagnosis of cervical precancerous lesions based on multimodal feature changes, Comput Biol Med, 130, 10.1016/j.compbiomed.2021.104209 Yuan, 2020, The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images, Sci Rep, 10, 1, 10.1038/s41598-020-68252-3 Yan, 2021, Multi-state colposcopy image fusion for cervical precancerous lesion diagnosis using {BF-CNN}, Biomed Signal Process Control, 68, 10.1016/j.bspc.2021.102700 Ito, 2022, An artificial intelligence-assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions, Mol Clin Oncol, 16, 27, 10.3892/mco.2021.2460 Jones, 2007, Colposcopic accuracy of obstetrics and gynecology residents: Commentary, Obstet Gynecol Surv, 62, 175, 10.1097/01.ogx.0000256806.44109.bc Slimani, 2016, Cyto-colpo-histologic correlation: About an analytical study of 120 colposcopies, Tunisie Medicale, 94, 616 Adsul, 2017, Implementing community-based cervical cancer screening programs using visual inspection with acetic acid in India: A systematic review, Cancer Epidemiol, 49, 161, 10.1016/j.canep.2017.06.008