ArticleFrontiers in surgery2022
A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- PRISM: a clinically interpretable stepwise framework for multimodal skin cancer diagnosis.Scientific reports · 2026Article
- Text guided cross attentive multimodal learning with visual feature modulation for automated skin lesion detection.Scientific reports · 2026Article
- Quantum-enhanced multimodal prognostic transformer for skin disease progression prediction and visualization.Scientific reports · 2026Article
- Multimodal Masked Autoencoder Based on Adaptive Masking for Vitiligo Stage Classification.Journal of imaging informatics in medicine · 2026Article
- SkinDet-YOLO: a context- and boundary-aware yolov8-based framework for skin disease detection.Frontiers in oncology · 2026Article
- Article
- Feasibility of multi-class dental caries detection using deep learning-based smartphone images: a pilot prospective study.Frontiers in oral health · 2026Article
- Review
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- AI in Aesthetic/Cosmetic Dermatology: Current and Future.Journal of cosmetic dermatology · 2025Review
- Comparative analysis of multimodal architectures for effective skin lesion detection using clinical and image data.Frontiers in artificial intelligence · 2025Article
- Decoding skin cancer classification: perspectives, insights, and advances through researchers' lens.Scientific reports · 2024Review
- Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
11 authors.
Funding
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Abstract
Introduction: Skin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion. We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones. Methods: A deep neural network was developed with two encoders for extracting information from image data and metadata. A multimodal fusion module with intra-modality self-attention and inter-modality cross-attention was proposed to effectively combine image features and meta features. The model was trained on tested on a public dataset and compared with other state-of-the-art methods using five-fold cross-validation. Results: Including metadata is shown to significantly improve a model's performance. Our model outperformed other metadata fusion methods in terms of accuracy, balanced accuracy and area under the receiver-operating characteristic curve, with an averaged value of 0.768±0.022, 0.775±0.022 and 0.947±0.007. Conclusion: A deep learning model using smartphone collected images and metadata for skin lesion diagnosis was successfully developed. The proposed model showed promising performance and could be a potential tool for skin cancer screening.
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