research Boundary-aware Multi-stage with Mobile U-Net for Hair Segmentation in Dermoscopic Images
The new method improves hair segmentation in skin images, helping detect skin cancer more accurately.
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research Crowdsourcing Dermatology Images with Google Search Ads: Creating a Real-World Skin Condition Dataset
Google Search ads effectively gathered a diverse dermatology image dataset for research and AI development.
research Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
The new method improves skin cancer detection in imbalanced datasets.
research Generative adversarial networks for anonymous acneic face dataset generation
The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.
research Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions
Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
research Research on hair removal algorithm of dermatoscopic images based on maximum variance fuzzy clustering and optimization Criminisi algorithm
The new hair removal algorithm for skin images works better for detecting and fixing hair, improving melanoma diagnosis.
research MSF-VMDNet for multi class segmentation of skin cancer whole slide images using a multi frequency dual encoder network
MSF-VMDNet accurately segments skin cancer images better than existing methods.
research A Comprehensive Survey on Vitiligo Detection Using Deep Learning
Better datasets and methods are needed for reliable vitiligo detection using deep learning.
research Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures
VGG16 and VGG19 are the most accurate for classifying scalp and hair diseases.
research Editorial: Progress and Prospects on Skin Imaging Technology, Teledermatology and Artificial Intelligence in Dermatology
New skin imaging, teledermatology, and AI could become key in future dermatology care.
research Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques
Researchers improved a skin disease diagnosis model using online images, achieving up to 49.64% accuracy.
research Use of modern technological devices (photography, mobile phone applications and artificial intelligence) in dermatology
Technology, like mobile apps and AI, is improving skin condition diagnosis and treatment.
research Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices
The AI tool helped primary care doctors and nurse practitioners diagnose skin conditions more accurately.
research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research Immunohistochemical Pattern of CD34 Distribution in Different Types of Basal Cell Carcinoma and in Peritumoral Skin
CD34 is absent in most basal cell carcinoma cells but present in surrounding skin.
research InCISE : Instrument for Comprehensive Incisional and Surgical Evaluation
The InCISE score is a promising tool for assessing wound healing in head and neck surgery but needs more research for broader use.
research 198 Reliable and interpretable segmentation for remote assessment of atopic dermatitis severity using digital images
The improved EczemaNet more reliably and clearly identifies and assesses the severity of atopic dermatitis from photos.
research Enhancing Low-Light Sports Motion Images with Improved Bilateral Filtering and Auto MSRCR
The method greatly improves low-light sports images' quality and reduces artifacts.
research ISID0197 - Quantitative analysis of hair loss in mice utilizing imageJ improves the efficacy evaluation of alopecia areata induction in the C3H/HeJ mouse model
A new image-based method improves accuracy in measuring hair loss in mice.
research Pre-trained classification of scalp conditions using image processing
The system can automatically classify scalp conditions with 85% accuracy.
research A discriminant multi-scale histopathology descriptor using dictionary learning
The new image descriptor helps identify skin cancer structures with good accuracy.
research 891 Comparison of phenotypes and transcriptomes of mouse skin-derived precursors and dermal mesenchymal stem cells
The search scheme SMRI is faster and more secure for retrieving encrypted data from the cloud.
research In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks
research Improved Hidden Markov Algorithm Based on Bayes in Low Dose CT Images
The improved algorithm enhances low-dose CT image quality significantly better than other methods.
research 42063 Image Quality Assessment using Convolutional Neural Network in Clinical Skin Images
The model can effectively identify good quality skin images but needs more testing for real-world use.
research Weakly-Supervised Hair SEM Microscope Image Segmentation Using a Priori Structure Information
The method improves hair image segmentation accuracy while reducing annotation costs.
research Deep skin diseases diagnostic system with Dual-channel Image and Extracted Text
The AI model DIET-AI effectively diagnoses skin diseases as well as doctors.
research scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data
scINSIGHT accurately identifies cell clusters and gene patterns in complex data.
research ISID0199 – Computer vision AI-based androgenetic alopecia analysis using a novel mobile web app.
research Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Reliable machine learning in medical imaging needs bias checks and data drift detection for consistent performance.