Integrated deep learning approach for generating cross-polarized images and analyzing skin melanin and hemoglobin distributions

Atef A, El-Rashidy MA, Azeem AA, Kabel AM. The role of stem cell factor in hyperpigmented skin lesions. Asian Pacific J Cancer Prev: APJCP. 2019;20(12):3723.

Article  Google Scholar 

Troyanova P, Borisova E, Avramov L. Fluorescence and reflectance properties of hemoglobin-pigmented skin disorders. In: International Conference on Lasers, Applications, and Technologies 2007: Laser Technologies for Medicine. vol. 6734. SPIE; 2007. pp. 142–149.

Gong H, Desvignes M. Hemoglobin and melanin quantification on skin images. In: Image Analysis and Recognition: 9th International Conference, ICIAR 2012, Aveiro, Portugal, June 25-27, 2012. Proceedings, Part II 9. Springer; 2012. pp. 198–205.

Goldsberry A, Hanke CW, Hanke KE. VISIA system: a possible tool in the cosmetic practice. J Drugs Dermatol: JDD. 2014;13(11):1312–4.

Google Scholar 

Linming F, Wei H, Anqi L, Yuanyu C, Heng X, Sushmita P, et al. Comparison of two skin imaging analysis instruments: the VISIA from Canfield vs. the ANTERA 3D CS from Miravex. Skin Res Technol. 2018;24(1):3–8.

Article  Google Scholar 

Dobrev H. Fluorescence diagnostic imaging in patients with acne. Photodermatol, Photoimmunol Photomed. 2010;26(6):285–9.

Article  Google Scholar 

Zonios G, Bykowski J, Kollias N. Skin melanin, hemoglobin, and light scattering properties can be quantitatively assessed in vivo using diffuse reflectance spectroscopy. J Invest Dermatol. 2001;117(6):1452–7.

Article  Google Scholar 

Jung B, Choi B, Durkin AJ, Kelly KM, Nelson JS. Characterization of port wine stain skin erythema and melanin content using cross-polarized diffuse reflectance imaging. Lasers Surg Med. 2004;34(2):174–81.

Article  Google Scholar 

Kojima K, Shido K, Tamiya G, Yamasaki K, Kinoshita K, Aiba S. Facial UV photo imaging for skin pigmentation assessment using conditional generative adversarial networks. Sci Rep. 2021;11(1):1213.

Article  Google Scholar 

Tsumura N, Ojima N, Sato K, Shiraishi M, Shimizu H, Nabeshima H, et al. Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin. In: ACM SIGGRAPH 2003 Papers. Association for Computing Machinery; 2003. pp. 770–779.

Jakovels D, Spigulis J, Saknite I. Multi-spectral mapping of in vivo skin hemoglobin and melanin. In: Biophotonics: Photonic Solutions for Better Health Care II. vol. 7715. SPIE; 2010. pp. 575–580.

Demirli R, Otto P, Viswanathan R, Patwardhan S, Larkey J. RBX® technology overview. Canfield Syst White Pap. 2007;1:1–5.

Google Scholar 

Tsumura N, Haneishi H, Miyake Y. Independent-component analysis of skin color image. JOSA A. 1999;16(9):2169–76.

Article  Google Scholar 

Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020;26(6):900–8.

Article  Google Scholar 

Kaymak R, Kaymak C, Ucar A. Skin lesion segmentation using fully convolutional networks: a comparative experimental study. Expert Syst Appl. 2020;161: 113742.

Article  Google Scholar 

Jha D, Smedsrud PH, Riegler MA, Johansen D, De Lange T, Halvorsen P. Resunet++: An advanced architecture for medical image segmentation. In: IEEE International Symposium on Multimedia (ISM). IEEE; 2019. pp. 225–2255.

Singh NK, Raza K. Medical image generation using generative adversarial networks: A review. Health informatics: A computational perspective in healthcare; 2021. pp. 77–96.

Tripathy S, Kannala J, Rahtu E. Learning image-to-image translation using paired and unpaired training samples. In: Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part II 14. Springer; 2019. pp. 51–66.

Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE; 2017. pp. 1125–1134.

Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. IEEE; 2017. pp. 2223–2232.

Wang TC, Liu MY, Zhu JY, Tao A, Kautz J, Catanzaro B. High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE; 2018. pp. 8798–8807.

Chen Q, Koltun V. Photographic image synthesis with cascaded refinement networks. In: Proceedings of the IEEE international conference on computer vision. IEEE; 2017. pp. 1511–1520.

Jung G, Lee J, Kim S. Spectrum-based deep learning framework for dermatological pigment analysis and simulation. Comput Biol Med. 2024;178: 108741. https://doi.org/10.1016/j.compbiomed.2024.108741.

Article  Google Scholar 

Jung G, Kim S, Lee J, Yoo S. Deep learning-based optical approach for skin analysis of melanin and hemoglobin distribution. J Biomed Optics. 2023;28(3): 035001.

Article  Google Scholar 

Jung G, Kim S, Lee J, Yoo S. Deep learning-based pigment analysis model trained with optical approach and ground truth assistance. J Biophotonics. 2023;16(12): e202300231.

Article  Google Scholar 

Jung G, Kim S, Lee J, Yoo S. Generation of skin tone and pigmented region-modified images using a pigment discrimination model trained with an optical approach. Skin Res Technol. 2023;29(10): e13486.

Article  Google Scholar 

Alotaibi S, Smith W. Biofacenet: Deep biophysical face image interpretation. arXiv preprint arXiv:1908.10578. 2019.

Xu C, Wang J, Yang W, Yu L. Dot distance for tiny object detection in aerial images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2021. pp. 1192–1201.

Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA. Albumentations: fast and flexible image augmentations. Information. 2020;11(2):125.

Article  Google Scholar 

Dolotov L, Sinichkin YP, Tuchin V, Utz S, Altshuler G, Yaroslavsky I. Design and evaluation of a novel portable erythema-melanin-meter. Lasers Surg Med. 2004;34(2):127–35.

Article  Google Scholar 

Setiadi DRIM. PSNR vs. SSIM: imperceptibility quality assessment for image steganography. Multimedia Tools Appl. 2021;80(6):8423–44.

Article  Google Scholar 

Jiang J, Liu D, Gu J, Süsstrunk S. What is the space of spectral sensitivity functions for digital color cameras? In: IEEE Workshop on Applications of Computer Vision (WACV). IEEE; 2013, pp. 168–79.

Puth MT, Neuhäuser M, Ruxton GD. Effective use of Pearson’s product-moment correlation coefficient. Anim Behav. 2014;93:183–9.

Article  Google Scholar 

Lee CC, Wu HC, Tsai CS, Chu YP. Adaptive lossless steganographic scheme with centralized difference expansion. Pattern Recogn. 2008;41(6):2097–106.

Article  Google Scholar 

Borji A. Pros and cons of GAN evaluation measures: new developments. Comput Vis Image Underst. 2022;215: 103329.

Article  Google Scholar 

Chanda T, Hauser K, Hobelsberger S, Bucher TC, Garcia CN, Wies C, et al. Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma. Nat Commun. 2024;15(1):524.

Article  Google Scholar 

Comments (0)

No login
gif