- Awesome-skin-lesion-diagnosis
- Biology
- Computer vision
- An updated review of clinical methods in the assessment of ageing skin – New perspectives and evaluation for claims support.Bielfeldt, S., et al.(International Journal of Cosmetic Science,2018)
- Evaluation of skin ageing: a systematic review of clinical scales. Dobos, G., et al.(Br J Dermatol 172, 2015).
- A Review of Ageing and an Examination of Clinical Methods in the Assessment of Ageing Skin. Part 2: Clinical Perspectives and Clinical Methods in the Evaluation of Ageing Skin. Callaghan, T. M., et al.(International Journal of Cosmetic Science,2008).
- HAM10000
- ISIC2019
- ISIC2020
- Skin disease diagnosis with deep learning: a review. Li, H., et al.(2020)
- Computational methods for pigmented skin lesion classification in images: review and future trends.Oliveira, R.B., et al.(J.M.R.S. Neural Comput & Applic,2018)
- Deep Learning in Skin Disease Image Recognition: A Review.Li, L.-F., et al.(2020)
- Towards a computer-aided diagnosis system for pigmented skin lesions
- 色斑检测图像分析系统的研究现状
- Joint Acne Image Grading and Counting via Label Distribution Learning.Wu, X., et al.(ICCV,2019)
- Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria. Yang, J., et al.(2018).
- On Out-of-Distribution Detection Algorithms With Deep Neural Skin Cancer Classifiers, Pacheco, et al.(2020)
- Out-of-Distribution Detection for Dermoscopic Image Classification.Mohseni,et al.(2021)
- Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest,Li, X., Lu, Y., Desrosiers, C., and Liu, X. (2020).
- Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning, Kim, H.,et al.(2021)
- Does your dermatology classifier know what it doesn’t know? Detecting the long-tail of unseen conditions, Guha Roy, et al.(2022).
- Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network, Ahmad, B., et al.(2020)
- Contrastive Representation for Dermoscopy Image Few-Shot Classification, Moxuan, et al.(2020)
- Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images, Codella, et al.(2018).
- Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features, Tschandl, et al.(2019).
- Dermoscopic image retrieval based on rotation-invariance deep hashing,(2021).
- Spatially constrained segmentation of dermoscopy images
- Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria. Yang, J., et al.(2018).
- Independent Component Analysis of Skin Color Image,N. Tsumura, et al.(Color and Imaging Conference, 1998)
- Image-based skin color and texture analysis/synhesis by extracting hemoglobin and melanin information in the skin,N. Tsumura et al.(ACM SIGGRAPH, 2003)
- Automatic skin decomposition based on single image, S. Xu, et al.(Computer Vision & Image Understanding, 2008).
- 一种基于人脸皮肤图像的色斑检测算法.储霞 et al.(微计算机信息,2009).
- 一种人脸皮肤图像诊断方法.李成龙 et al,(计算机工程与应用,2012)
- Skin Pigment Recognition using Projective Hemoglobin- Melanin Coordinate Measurements. Yang, L., et al(2016).
- Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images. (Sci Transl Med,2021).
- RethNet: Object-by-Object Learning for Detecting Facial Skin Problems. Bekmirzaev, S., et al.(ICCVW, 2019)
- Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network. Han, S.S., et al. (JAMA Dermatol ,2020)
- Skin Lesion Detection Algorithms in Whole Body Images. Strzelecki, M.H., et al.(Sensors, 2021).
- Can self-training identify suspicious ugly duckling lesions?, Mohseni, M., et al(ArXiv:2105.07116., 2021)
- Robust Estimation of Skin Pigmentation from Facial Color Images Based on Color Constancy. Liu, X., et al. (ICMTMA,2018).
- Skin-pigmentation-disorder detection algorithm based on projective coordinates. Liu, Y., et al.(Optik,2016).
- Image-processing based facial imperfection region detection and segmentation
- Skin lesion tracking using structured graphical models. (Medical Image Analysis,2016).