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- Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review
- Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study
- Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures
- Generative adversarial networks for anonymous acneic face dataset generation
- Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
- Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
- Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue
- Hair & Scalp Disease Detection Using Machine Learning & Image Processing
- Quantitative analysis and development of alopecia areata classification frameworks
- Hair and scalp disease detection using deep learning
- AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata
- Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study
- From Diagnosis to Prognosis: A Scoping Review of Artificial Intelligence in Alopecia Research
- Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
- Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
- Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
- Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
- Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
- Trichoscopy and Computational Models for Hair and Scalp Disorders: Image Analysis, Quantification, and Clinical Integration
- Automating Hair Loss Labels for Universally Scoring Alopecia From Images
- NLKFill: high-resolution image inpainting with a novel large kernel attention
- BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks and Clinical Features
- MSF-VMDNet for multi class segmentation of skin cancer whole slide images using a multi frequency dual encoder network
- Hair cluster detection model based on dermoscopic images
- Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
- Effective holistic characterization of small molecule effects using heterogeneous biological networks
- Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks
- A Novel Fusion Technique for Early Detection of Alopecia Areata Using ResNet-50 and CRSHOG
- Multi-omics and AI-driven advances in miRNA-mediated hair follicle regulation in cashmere goats
- Leucine-Rich Repeat-Containing G-Protein-Coupled Receptors as Markers of Adult Stem Cells