ArticleBioengineering (Basel, Switzerland)2024
EfficientUNetViT: Efficient Breast Tumor Segmentation Utilizing UNet Architecture and Pretrained Vision Transformer.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in breast cancer research: a systematic review and bibliometric analysis of emerging trends and future directions.Frontiers in oncology · 2026Pooled it
- Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2026Pooled it
- A Hybrid Model for Ultrasound Image-Based Breast Cancer Diagnosis Using EfficientNet-V2 and Vision Transformer.Diagnostics (Basel, Switzerland) · 2026Article
- MassSeg-Framework: A Breast Mass Detection and Segmentation Framework Based on Deep Learning and an Active Contour Model.Life (Basel, Switzerland) · 2026Article
- Brain tumor detection on magnetic resonance imaging scans using the artificial intelligence-based You Only Look Once algorithm.The Journal of international medical research · 2026Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Enhanced stroke risk prediction in hypertensive patients through deep learning integration of imaging and clinical data.BMC medical informatics and decision making · 2025Article
- Multi channel fusion diffusion models for brain tumor MRI data augmentation.Scientific reports · 2025Article
- Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.Scientific reports · 2025Article
- Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models.Scientific reports · 2025Article
- Self-supervised learning and transformer-based technologies in breast cancer imaging.Frontiers in radiology · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
This study introduces a sophisticated neural network structure for segmenting breast tumors. It achieves this by combining a pretrained Vision Transformer (ViT) model with a UNet framework. The UNet architecture, commonly employed for biomedical image segmentation, is further enhanced with depthwise separable convolutional blocks to decrease computational complexity and parameter count, resulting in better efficiency and less overfitting. The ViT, renowned for its robust feature extraction capabilities utilizing self-attention processes, efficiently captures the overall context within images, surpassing the performance of conventional convolutional networks. By using a pretrained ViT as the encoder in our UNet model, we take advantage of its extensive feature representations acquired from extensive datasets, resulting in a major enhancement in the model's ability to generalize and train efficiently. The suggested model has exceptional performance in segmenting breast cancers from medical images, highlighting the advantages of integrating transformer-based encoders with efficient UNet topologies. This hybrid methodology emphasizes the capabilities of transformers in the field of medical image processing and establishes a new standard for accuracy and efficiency in activities related to tumor segmentation.
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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.