ArticleDiagnostics (Basel, Switzerland)2022
BUViTNet: Breast Ultrasound Detection via Vision Transformers.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches.Biomedical engineering online · 2026Pooled it
- Dual-discriminator network-based classification method for breast ultrasound imaging.Quantitative imaging in medicine and surgery · 2026Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- Deep learning-based radiomics and pathomics for decoding tumor microenvironment and predicting immunotherapy outcomes in gastric cancer.Frontiers in immunology · 2026Review
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- A multimodal fusion model integrating Vision Transformer, radiomics, and clinical features for predicting bone metastasis in prostate cancer.Frontiers in oncology · 2026Article
- Impact of differential privacy on breast ultrasound image classification performance using vision transformer.Quantitative imaging in medicine and surgery · 2025Article
- A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis.BMC medical informatics and decision making · 2025Article
- Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative analysis of convolutional neural networks and vision transformers in identifying benign and malignant breast lesions.Medicine · 2025Article
- Role of Artificial Intelligence in Thyroid Cancer Diagnosis.Journal of clinical medicine · 2025Review
- MammoViT: A Custom Vision Transformer Architecture for Accurate BIRADS Classification in Mammogram Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Identification of benign and malignant breast nodules on ultrasound: comparison of multiple deep learning models and model interpretation.Frontiers in oncology · 2025Article
- Exploring AI Approaches for Breast Cancer Detection and Diagnosis: A Review Article.Breast cancer (Dove Medical Press) · 2025Review
- Self-supervised learning and transformer-based technologies in breast cancer imaging.Frontiers in radiology · 2025Review
- BCED-Net: Breast Cancer Ensemble Diagnosis Network using transfer learning and the XGBoost classifier with mammography images.Osong public health and research perspectives · 2024Article
- Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer.NPJ precision oncology · 2024Article
- Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.Surgery today · 2024Article
- Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer.Cancers · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Convolutional neural networks (CNNs) have enhanced ultrasound image-based early breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the most effective method for natural image analysis. ViTs have proven their capability of incorporating more global information than CNNs at lower layers, and their skip connections are more powerful than those of CNNs, which endows ViTs with superior performance. However, the effectiveness of ViTs in breast ultrasound imaging has not yet been investigated. Here, we present BUViTNet breast ultrasound detection via ViTs, where ViT-based multistage transfer learning is performed using ImageNet and cancer cell image datasets prior to transfer learning for classifying breast ultrasound images. We utilized two publicly available ultrasound breast image datasets, Mendeley and breast ultrasound images (BUSI), to train and evaluate our algorithm. The proposed method achieved the highest area under the receiver operating characteristics curve (AUC) of 1 ± 0, Matthew’s correlation coefficient (MCC) of 1 ± 0, and kappa score of 1 ± 0 on the Mendeley dataset. Furthermore, BUViTNet achieved the highest AUC of 0.968 ± 0.02, MCC of 0.961 ± 0.01, and kappa score of 0.959 ± 0.02 on the BUSI dataset. BUViTNet outperformed ViT trained from scratch, ViT-based conventional transfer learning, and CNN-based transfer learning in classifying breast ultrasound images (p < 0.01 in all cases). Our findings indicate that improved transformers are effective in analyzing breast images and can provide an improved diagnosis if used in clinical settings. Future work will consider the use of a wide range of datasets and parameters for optimized performance.
Indexed as
Identifiers
What OpenQuestion holds
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.