Utilizing deep learning for dermal matrix quality assessment on in vivo line‐field confocal optical coherence tomography images
暂无分享,去创建一个
J. Breugnot | B. Closs | S. Bordes | E. Aymard | P. Rouaud-Tinguely | Sophie Gilardeau | Delphine Rondeau
[1] P. Kopnin,et al. Age-Related Changes in the Fibroblastic Differon of the Dermis: Role in Skin Aging , 2022, International journal of molecular sciences.
[2] N. Mazumder,et al. Deep learning-based image processing in optical microscopy , 2022, Biophysical Reviews.
[3] João Beirão,et al. A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management , 2022, Medicina.
[4] J. Malvehy,et al. Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning , 2022 .
[5] M. Lupu,et al. Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology , 2022, Journal of clinical medicine.
[6] Amrit Greene,et al. Dermoscopy in Primary Care. , 2022, Primary care.
[7] Ammar H. Elsheikh,et al. Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm , 2021, Entropy.
[8] J. Malvehy,et al. Line‐field confocal optical coherence tomography as a tool for three‐dimensional in vivo quantification of healthy epidermis: A pilot study , 2021, Journal of biophotonics.
[9] J. Vorstenbosch,et al. Current Advances in Hypertrophic Scar and Keloid Management , 2021, Seminars in Plastic Surgery.
[10] Emelina P. Vienneau,et al. Seeing through the Skin: Photoacoustic Tomography of Skin Vasculature and Beyond , 2021, JID innovations.
[11] A. Dubois,et al. Line-field confocal optical coherence tomography for three-dimensional skin imaging , 2020, Frontiers of Optoelectronics.
[12] Julia Welzel,et al. Line‐field confocal optical coherence tomography—Practical applications in dermatology and comparison with established imaging methods , 2020, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.
[13] Di Zhao,et al. A review of the application of deep learning in medical image classification and segmentation , 2020, Annals of translational medicine.
[14] Milind Rajadhyaksha,et al. Segmentation of Cellular Patterns in Confocal Images of Melanocytic Lesions in vivo via a Multiscale Encoder-Decoder Network (MED-Net) , 2020, Medical Image Anal..
[15] A. Martin,et al. Unraveling the molecular and cellular mechanisms of stretch marks , 2020, Journal of cosmetic dermatology.
[16] Jennifer G. Dy,et al. Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy. , 2019, The Journal of investigative dermatology.
[17] Arthur J. Davis,et al. Comparison of line‐field confocal optical coherence tomography images with histological sections: Validation of a new method for in vivo and non‐invasive quantification of superficial dermis thickness , 2019, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.
[18] Á. Juarranz,et al. Environmental Stressors on Skin Aging. Mechanistic Insights , 2019, Front. Pharmacol..
[19] Quoc V. Le,et al. Searching for MobileNetV3 , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[20] Hugues Talbot,et al. Three-dimensional conditional random field for the dermal–epidermal junction segmentation , 2019, Journal of medical imaging.
[21] A. Rossi,et al. Emerging imaging technologies in dermatology: Part II: Applications and limitations , 2019, Journal of the American Academy of Dermatology.
[22] P. Aarabi,et al. A new procedure, free from human assessment that automatically grades some facial skin structural signs. Comparison with assessments by experts, using referential atlases of skin ageing , 2019, International journal of cosmetic science.
[23] Arthur J. Davis,et al. Simultaneous dual-band line-field confocal optical coherence tomography: application to skin imaging. , 2019, Biomedical optics express.
[24] N. Fortunel,et al. Age-related evolutions of the dermis: Clinical signs, fibroblast and extracellular matrix dynamics , 2019, Mechanisms of Ageing and Development.
[25] Arthur J. Davis,et al. Line-field confocal time-domain optical coherence tomography with dynamic focusing. , 2018, Optics express.
[26] A. Rossi,et al. Emerging imaging technologies in dermatology: Part I: Basic principles , 2018, Journal of the American Academy of Dermatology.
[27] S. Daveluy,et al. OCT image atlas of healthy skin on sun‐exposed areas , 2018, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.
[28] Jean-Luc Perrot,et al. Line-field confocal optical coherence tomography for high-resolution noninvasive imaging of skin tumors , 2018, Journal of biomedical optics.
[29] R. Sivamani,et al. Acne Scars: How Do We Grade Them? , 2018, American Journal of Clinical Dermatology.
[30] J. Voorhees,et al. Extracellular matrix regulation of fibroblast function: redefining our perspective on skin aging , 2018, Journal of Cell Communication and Signaling.
[31] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[32] V. Newton,et al. Skin aging: molecular pathology, dermal remodelling and the imaging revolution. , 2015, Giornale italiano di dermatologia e venereologia : organo ufficiale, Societa italiana di dermatologia e sifilografia.
[33] T. Jørgensen,et al. Optical coherence tomography in dermatology , 2006 .
[34] T. Quan,et al. Role of Age-Associated Alterations of the Dermal Extracellular Matrix Microenvironment in Human Skin Aging: A Mini-Review , 2015, Gerontology.
[35] Kishan Dholakia,et al. Multi-modal approach using Raman spectroscopy and optical coherence tomography for the discrimination of colonic adenocarcinoma from normal colon. , 2013, Biomedical optics express.
[36] Beibei Cheng,et al. Analysis of clinical and dermoscopic features for basal cell carcinoma neural network classification , 2013, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.
[37] H. Maibach,et al. Characteristics of the Aging Skin. , 2013, Advances in wound care.
[38] Michael J Sherratt,et al. Molecular aspects of skin ageing. , 2011, Maturitas.
[39] K. Glanz,et al. Measures of sun exposure and sun protection practices for behavioral and epidemiologic research. , 2008, Archives of dermatology.
[40] T. Mack,et al. The objective assessment of lifetime cumulative ultraviolet exposure for determining melanoma risk. , 2006, Journal of photochemistry and photobiology. B, Biology.
[41] S. Rosso,et al. Reproducibility of skin characteristic measurements and reported sun exposure history. , 2002, International journal of epidemiology.
[42] Kristin J. Dana,et al. Hybrid deep learning for Reflectance Confocal Microscopy skin images , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[43] A. Mamalis,et al. Optical coherence tomography (OCT) of collagen in normal skin and skin fibrosis , 2013, Archives of Dermatological Research.
[44] Joseph P. Near,et al. How to cite this article , 2011 .