ArticleNPJ breast cancer2021
Connected-UNets: a deep learning architecture for breast mass segmentation.
Article in NPJ breast cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 149 citations in OpenAlex.
- MB-SwinRefiner: Mammographic Mass Segmentation with Probability-Guided Residual Refinement.Journal of imaging · 2026Article
- MassSeg-Framework: A Breast Mass Detection and Segmentation Framework Based on Deep Learning and an Active Contour Model.Life (Basel, Switzerland) · 2026Article
- Fractional Active Contour Model with Adaptive Hessian Weighting for Mammogram Segmentation.Journal of medical signals and sensors · 2026Article
- A Two-Stage Lightweight Deep Learning Framework for Mass Detection and Segmentation in Mammograms Using YOLOv5 and Depthwise SegNet.Journal of imaging informatics in medicine · 2025Article
- A multi stage deep learning model for accurate segmentation and classification of breast lesions in mammography.Scientific reports · 2025Article
- Sustainable deep learning-based breast lesion segmentation: impact of breast region segmentation on performance.BMC medical imaging · 2025Article
- Dual-Tree Complex Wavelet Pooling and Attention-Based Modified U-Net Architecture for Automated Breast Thermogram Segmentation and Classification.Journal of imaging informatics in medicine · 2025Article
- Dual-channel compression mapping network with fused attention mechanism for medical image segmentation.Scientific reports · 2025Article
- Advancing breast cancer relapse prediction with radiomics and neural networks: a clinically interpretable framework.Frontiers in oncology · 2025Article
- Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset.Scientific reports · 2024Article
- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
- Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis.Biomedical engineering letters · 2024Article
- Improved Loss Function for Mass Segmentation in Mammography Images Using Density and Mass Size.Journal of imaging · 2024Article
- Presegmenter Cascaded Framework for Mammogram Mass Segmentation.International journal of biomedical imaging · 2024Article
- AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net.PeerJ. Computer science · 2024Article
- Article
- A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Article
- Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.European radiology · 2023Article
- K2S Challenge: From Undersampled K-Space to Automatic Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- DRI-Net: segmentation of polyp in colonoscopy images using dense residual-inception network.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for breast mass segmentation to assist radiologists in their diagnosis. With the rapid development of deep learning and its application to medical imaging challenges, UNet and its variations is one of the state-of-the-art models for medical image segmentation that showed promising performance on mammography. In this paper, we propose an architecture, called Connected-UNets, which connects two UNets using additional modified skip connections. We integrate Atrous Spatial Pyramid Pooling (ASPP) in the two standard UNets to emphasize the contextual information within the encoder-decoder network architecture. We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). We evaluated the proposed architectures on two publically available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Experiments were also conducted using additional synthetic data using the cycle-consistent Generative Adversarial Network (CycleGAN) model between two unpaired datasets to augment and enhance the images. Qualitative and quantitative results show that the proposed architecture can achieve better automatic mass segmentation with a high Dice score of 89.52%, 95.28%, and 95.88% and Intersection over Union (IoU) score of 80.02%, 91.03%, and 92.27%, respectively, on CBIS-DDSM, INbreast, and the private dataset.
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