ArticleThe Visual computer2022
A multimodal transformer to fuse images and metadata for skin disease classification.
Article in The Visual computer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 142 citations in OpenAlex.
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- Multisequence MRI and clinical data-based deep learning radiomics model for predicting adjacent segment degeneration post-lumbar fusion: a retrospective multicenter study.BMC medical imaging · 2026Article
- Research on Alzheimer's disease MRI image classification based on spatial attention mechanism.Frontiers in aging neuroscience · 2026Article
- Hierarchical spatio-temporal graph network for risk prediction.Scientific reports · 2025Article
- Review
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Multimodal dual-stage feature refinement for robust skin lesion classification.Scientific reports · 2025Article
- ViT-BiLSTM Multimodal Learning for Paediatric ADHD Recognition: Integrating Wearable Sensor Data with Clinical Profiles.Sensors (Basel, Switzerland) · 2025Article
- Intelligent diagnosis model for chest X-ray images diseases based on convolutional neural network.BMC medical imaging · 2025Article
- MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features.Journal of imaging informatics in medicine · 2025Article
- Enhanced hierarchical attention mechanism for mixed MIL in automatic Gleason grading and scoring.Scientific reports · 2025Article
- Minimal sourced and lightweight federated transfer learning models for skin cancer detection.Scientific reports · 2025Article
- Attention-aware Deep Learning Models for Dermoscopic Image Classification for Skin Disease Diagnosis.Current medical imaging · 2025Article
- Article
- Advances in intelligent recognition and diagnosis of skin scar images: concepts, methods, challenges, and future trends.Frontiers in medicine · 2025Review
- Skin disease diagnosis using decision and feature level fusion of deep features.Frontiers in digital health · 2025Article
- Impact of metadata in multimodal classification of bone tumours.BMC musculoskeletal disorders · 2024Article
- Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions.Sensors (Basel, Switzerland) · 2024Article
- Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications.PeerJ. Computer science · 2024Article
- Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin disease cases are rising in prevalence, and the diagnosis of skin diseases is always a challenging task in the clinic. Utilizing deep learning to diagnose skin diseases could help to meet these challenges. In this study, a novel neural network is proposed for the classification of skin diseases. Since the datasets for the research consist of skin disease images and clinical metadata, we propose a novel multimodal Transformer, which consists of two encoders for both images and metadata and one decoder to fuse the multimodal information. In the proposed network, a suitable Vision Transformer (ViT) model is utilized as the backbone to extract image deep features. As for metadata, they are regarded as labels and a new Soft Label Encoder (SLE) is designed to embed them. Furthermore, in the decoder part, a novel Mutual Attention (MA) block is proposed to better fuse image features and metadata features. To evaluate the model's effectiveness, extensive experiments have been conducted on the private skin disease dataset and the benchmark dataset ISIC 2018. Compared with state-of-the-art methods, the proposed model shows better performance and represents an advancement in skin disease diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.