ArticleVisual computing for industry, biomedicine, and art2024
Multi-task approach based on combined CNN-transformer for efficient segmentation and classification of breast tumors in ultrasound images.
Article in Visual computing for industry, biomedicine, and art, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 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
- N-Unet: An Efficient Multi-Task Model for Precise Classification and Segmentation of Breast Ultrasound Images.Journal of imaging · 2026Article
- A Hybrid Model for Ultrasound Image-Based Breast Cancer Diagnosis Using EfficientNet-V2 and Vision Transformer.Diagnostics (Basel, Switzerland) · 2026Article
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- A multi-scale hybrid ResNet-transformer with distance-aware learning for interpretable BI-RADS mammographic classification.Scientific reports · 2026Article
- RepACNet: A Lightweight Reparameterized Asymmetric Convolution Network for Monocular Depth Estimation.Sensors (Basel, Switzerland) · 2026Article
- Cross-attention guided multi-modal network for breast ultrasound diagnosis incorporating objective clinical semantics.Frontiers in medicine · 2026Article
- Multi-scale information bottleneck with confidence-weighted decision fusion for robust breast ultrasound lesion classification.Frontiers in oncology · 2026Article
- Artificial Intelligence-Based Wearable Sensing Technologies for the Management of Cancer, Diabetes, and COVID-19.Biosensors · 2025Review
- A hybrid attention network for accurate breast tumor segmentation in ultrasound images.Scientific reports · 2025Article
- Research on Wind Field Correction Method Integrating Position Information and Proxy Divergence.Biomimetics (Basel, Switzerland) · 2025Article
- A novel approach for breast cancer detection using a Nesterov accelerated adam optimizer with an attention mechanism.Scientific reports · 2025Article
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
- MDiagnostics (Basel, Switzerland) · 2025Article
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models.Scientific reports · 2025Article
- Patho-Net: enhancing breast cancer classification using deep learning and explainable artificial intelligence.American journal of cancer research · 2025Article
- EfficientUNetViT: Efficient Breast Tumor Segmentation Utilizing UNet Architecture and Pretrained Vision Transformer.Bioengineering (Basel, Switzerland) · 2024Article
- Correction: Multi-task approach based on combined CNN-transformer for efficient segmentation and classification of breast tumors in ultrasound images.Visual computing for industry, biomedicine, and art · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate segmentation of breast ultrasound (BUS) images is crucial for early diagnosis and treatment of breast cancer. Further, the task of segmenting lesions in BUS images continues to pose significant challenges due to the limitations of convolutional neural networks (CNNs) in capturing long-range dependencies and obtaining global context information. Existing methods relying solely on CNNs have struggled to address these issues. Recently, ConvNeXts have emerged as a promising architecture for CNNs, while transformers have demonstrated outstanding performance in diverse computer vision tasks, including the analysis of medical images. In this paper, we propose a novel breast lesion segmentation network CS-Net that combines the strengths of ConvNeXt and Swin Transformer models to enhance the performance of the U-Net architecture. Our network operates on BUS images and adopts an end-to-end approach to perform segmentation. To address the limitations of CNNs, we design a hybrid encoder that incorporates modified ConvNeXt convolutions and Swin Transformer. Furthermore, to enhance capturing the spatial and channel attention in feature maps we incorporate the Coordinate Attention Module. Second, we design an Encoder-Decoder Features Fusion Module that facilitates the fusion of low-level features from the encoder with high-level semantic features from the decoder during the image reconstruction. Experimental results demonstrate the superiority of our network over state-of-the-art image segmentation methods for BUS lesions segmentation.
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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.