ArticleJournal of cancer research and clinical oncology2023
MDFNet: application of multimodal fusion method based on skin image and clinical data to skin cancer classification.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 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
- Image-Driven Multimodal Deep Learning for Skin Cancer Diagnosis: Cross-Attention Fusion of Dermoscopic Imaging Clinical Data and Knowledge Graphs.Sensors (Basel, Switzerland) · 2026Article
- A Real-Time Mobile AI-Assisted System for Skin Disease Prescreening: A Technical Feasibility Study.Health science reports · 2026Article
- Multimodal Deep Learning with Attention-Based Fusion for Skin Cancer Diagnosis.Bioengineering (Basel, Switzerland) · 2026Article
- A brain-inspired computational framework for image-based risk assessment.Scientific reports · 2026Article
- DermaCalibra: A Robust and Explainable Multimodal Framework for Skin Lesion Diagnosis via Bayesian Uncertainty and Dynamic Modulation.Diagnostics (Basel, Switzerland) · 2026Article
- Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.Brain informatics · 2026Review
- Multimodal Masked Autoencoder Based on Adaptive Masking for Vitiligo Stage Classification.Journal of imaging informatics in medicine · 2026Article
- Federated learning for privacy-preserving skin cancer classification using deep neural networks.Frontiers in oncology · 2026Article
- Review
- Transfer Learning-Based Ensemble of CNNs and Vision Transformers for Accurate Melanoma Diagnosis and Image Retrieval.Diagnostics (Basel, Switzerland) · 2025Article
- Review of Non-Invasive Imaging Technologies for Cutaneous Melanoma.Biosensors · 2025Review
- A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP.Journal of imaging informatics in medicine · 2024Article
- CTDUNet: A Multimodal CNN-Transformer Dual U-Shaped Network with Coordinate Space Attention forPlants (Basel, Switzerland) · 2024Article
- Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.Archives of dermatological research · 2024Article
- Deep learning-based multimodal spatial transcriptomics analysis for cancer.Advances in cancer research · 2024Review
- A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata.Frontiers in surgery · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
purposeSkin cancer is one of the ten most common cancer types in the world. Early diagnosis and treatment can effectively reduce the mortality of patients. Therefore, it is of great significance to develop an intelligent diagnosis system for skin cancer. According to the survey, at present, most intelligent diagnosis systems of skin cancer only use skin image data, but the multi-modal cross-fusion analysis using image data and patient clinical data is limited. Therefore, to further explore the complementary relationship between image data and patient clinical data, we propose multimode data fusion diagnosis network (MDFNet), a framework for skin cancer based on data fusion strategy.
methodsMDFNet establishes an effective mapping among heterogeneous data features, effectively fuses clinical skin images and patient clinical data, and effectively solves the problems of feature paucity and insufficient feature richness that only use single-mode data.
resultsThe experimental results present that our proposed smart skin cancer diagnosis model has an accuracy of 80.42%, which is an improvement of about 9% compared with the model accuracy using only medical images, thus effectively confirming the unique fusion advantages exhibited by MDFNet.
conclusionsThis illustrates that MDFNet can not only be applied as an effective auxiliary diagnostic tool for skin cancer diagnosis, help physicians improve clinical decision-making ability and effectively improve the efficiency of clinical medicine diagnosis, but also its proposed data fusion method fully exerts the advantage of information convergence and has a certain reference value for the intelligent diagnosis of numerous clinical diseases.
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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.