[1] X. Zhao et al. Combining photothermal ablation-based vaccine with immune checkpoint blockade for synergistic osteosarcoma immunotherapy. Mater. Des., 198, 109311(2021).
[2] A. Attia, A. Siriwardana, D. Desai. Sarcomatoid urothelial carcinoma of the ureter with heterologous elements of chondrosarcoma and osteosarcoma, and concurrent divergent squamous differentiation: A rare case report. Urol. Case Rep., 34, 101484(2021).
[3] A. Stylianou et al. Pancreatic cancer collagen-based optical signatures. Proc. SPIE, 11646, 1164613(2021).
[4] A. K. Shah. Postoperative pathologic assessment of surgical margins in oral cancer — A contemporary review. J. Oral Maxillofac. Pathol., 22, 78-85(2018).
[5] S. Mahima, S. Chitra, N. U. Sujatha. Optical polarization response of collagen: role in clinical cancer diagnostics — part I. ISSS J. Micro Smart Syst., 11, 3-30(2022).
[6] M. Fang et al. Collagen as a double-edged sword in tumor progression. Tumour Biol., 35, 2871-2882(2014).
[7] Y. Dong et al. Quantitatively characterizing the microstructural features of breast ductal carcinoma tissues in different progression stages by Mueller matrix microscope. Biomed. Opt. Express, 8, 3643-3655(2017).
[8] A. C. M. Cavaco et al. Collagen biology making inroads into prognosis and treatment of cancer progression and metastasis. Cancer Metastasis Rev., 39, 603-623(2020).
[9] A. Golaraei et al. Changes of collagen ultrastructure in breast cancer tissue determined by second-harmonic generation double Stokes-Mueller polarimetric microscopy. Biomed. Opt. Express, 7, 4054-4068(2016).
[10] J. N. Ouellette et al. Navigating the collagen jungle: The biomedical potential of fiber organization in cancer. Bioengineering, 8, 17(2021).
[11] J. Westreich et al. Novel methodology to image stromal tissue and assess its morphological features with polarized light: towards a tumour microenvironment prognostic signature. Biomed. Opt. Express, 10, 3963-3973(2019).
[12] M. W. Conklin et al. Fluorescence lifetime imaging of endogenous fluorophores in histopathology sections reveals differences between normal and tumor epithelium in carcinoma in situ of the breast. Cell Biochem. Biophys., 53, 145-157(2009).
[13] C. Stringari et al. Multicolor two-photon imaging of endogenous fluorophores in living tissues by wavelength mixing. Sci. Rep., 7, 3792(2017).
[14] K. Pandey et al. Fluorescence spectroscopy: A new approach in cervical cancer. J. Obstet. Gynaecol. India, 62, 432-436(2012).
[15] J. Wu et al. Multimodal microscopic imaging with deep learning for highly effective diagnosis of breast cancer. Opt. Lasers Eng., 168, 107667(2023).
[16] K. B. Johnson, D. H. Ferguson, A. C. Nix. Use of convolutional neural network image classification and high-speed ion probe data towards real-time detonation characterization in a water-cooled rotating detonation engine(2022).
[17] M. A. Marchetti et al. Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J. Am. Acad. Dermatol., 78, 270-277.e1(2017).
[18] O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional networks for biomedical image segmentation, 9351, 234-241(2015).
[19] N. Ali et al. Automatic label-free detection of breast cancer using nonlinear multimodal imaging and the convolutional neural network ResNet50. Transl. Biophotonics, 1, e201900003(2019).
[20] T. Meyer et al. Multimodal nonlinear microscopic investigations on head and neck squamous cell carcinoma: Toward intraoperative imaging. Head Neck, 35, E280-E287(2013).
[21] A. V. Parwani. Next generation diagnostic pathology: Use of digital pathology and artificial intelligence tools to augment a pathological diagnosis. Diagn. Pathol., 14, 138(2019).
[22] T. Meyer et al. Nonlinear microscopy, infrared, and Raman microspectroscopy for brain tumor analysis. J. Biomed. Opt., 16, 021113(2011).
[23] G. Liu et al. BI-RADS 4 breast lesions: could multi-mode ultrasound be helpful for their diagnosis?. Gland Surg., 8, 258-270(2019).
[24] X. Leng et al. Role of multi-mode ultrasound in the diagnosis of level 4 BI-RADS breast lesions and Logistic regression model. Int. J. Clin. Exp. Med., 8, 15889-15899(2015).
[25] S. Li et al. Pixel-level image fusion: A survey of the state of the art. Inf. Fus., 33, 100-112(2017).
[26] Q. Wang, X.-J. Li, J. Chen. Research on image classification based on HE-Net convolutional neural networks. J. Chengdu Univ. Inf. Technol., 2017, 503-507(2017).
[27] N. A. Farda et al. Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques. Injury, 52, 616-624(2020).
[28] X. Wu, J. Zhang, X. Xu. Hand gesture recognition algorithm based on Faster R-CNN. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/J. Comput.-Aided Des. Comput. Graph., 30, 468-476(2018).
[29] M. Gao et al. A transfer residual neural network based on ResNet-34 for detection of wood knot defects. Forests, 12, 212(2021).
[30] D. Hu et al. Statistical inference for the two-sample problem under likelihood ratio ordering, with application to the ROC curve estimation. Stat. Med., 42, 3649-3664(2023).
[31] M. Zaffar, A. Pradhan. Assessment of anisotropy of collagen structures through spatial frequencies of Mueller matrix images for cervical pre-cancer detection. Appl. Opt., 59, 1237-1248(2020).
[32] Y. Dong et al. Deriving polarimetry feature parameters to characterize microstructural features in histological sections of breast tissues. IEEE Trans. Biomed. Eng., 68, 881-892(2021).
[33] M. Marina et al. Inflammation, extracellular matrix remodeling, and proteostasis in tumor microenvironment. Int. J. Mol. Sci., 22, 8102(2021).
[34] R. P. Lieli, Y. C. Hsu. Using the area under an estimated ROC curve to test the adequacy of binary predictors. J. Nonparametric Stat., 31, 100-130(2018).
