ArticleTechnology in cancer research & treatment
A Hybrid CNN-Transformer Deep Learning Model for Differentiating Benign and Malignant Breast Tumors Using Multi-View Ultrasound Images.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
IntroductionConventional single-image analysis often fails to capture the comprehensive features of breast lesions, limiting the diagnostic accuracy of ultrasound. This study introduces a computer-aided diagnosis (CAD) system that emulates the clinical multi-view diagnostic process to improve reliability.MethodsA novel hybrid deep learning model was proposed for breast cancer diagnosis task using multi-view ultrasound images, which integrates a Convolutional Neural Network (CNN) module with a Transformer-based module. The pre-trained EfficientNetV2 was used to extract spatial features, and the Transformer-based encoder was applied to fuse information from multiple, unordered ultrasound images of a single lesion. This retrospective study utilized a primary internal dataset from 840 patients and an independent external dataset from 133 patients. A strict lesion-level data partitioning strategy was implemented to prevent data leakage. The performance of the proposed model was evaluated against a baseline single-image model, advanced architectures, and human experts, followed by validation on a prospective dataset of 188 lesions.ResultsOn the internal test set, the hybrid CNN-Transformer achieved an accuracy with 0.960, a sensitivity with 0.967, and an Area Under the Curve (AUC) with 0.9788. On the external dataset, it demonstrated robust generalization with 0.940 accuracy and 0.9730 AUC. Prospective validation confirmed these findings with an accuracy of 0.952 and an AUC of 0.9801. The proposed model outperforms the single-image baseline with 0.881 internal accuracy and 0.849 accuracy of a senior ultrasound physician.ConclusionThe system combines CNNs with Transformers to analyze multi-view ultrasound images, achieving high diagnostic accuracy and robustness. By fusing multi-view information, this model aligns with clinical logic and outperforms both single-image methods and human experts.
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.