ArticlePloS one2018
Automated and real-time segmentation of suspicious breast masses using convolutional neural network.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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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.
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Who cites it
36 citing papers in PubMed.
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?European radiology experimental · 2024Article
- A Review of Artificial Intelligence in Breast Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- 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
- Application of an artificial intelligence-based system in the diagnosis of breast ultrasound images obtained using a smartphone.World journal of surgical oncology · 2024Article
- DMAeEDNet: Dense Multiplicative Attention Enhanced Encoder Decoder Network for Ultrasound-Based Automated Breast Lesion Segmentation.IEEE access : practical innovations, open solutions · 2024Article
- Artificial intelligence in breast imaging: Current situation and clinical challenges.Exploration (Beijing, China) · 2023Review
- Memory-efficient low-compute segmentation algorithms for bladder-monitoring smart ultrasound devices.Scientific reports · 2023Article
- Objective assessment of segmentation models for thyroid ultrasound images.Journal of ultrasound · 2023Article
- Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.Journal of cancer research and clinical oncology · 2023Review
- Review
- An Automatic Breast Tumor Detection and Classification including Automatic Tumor Volume Estimation Using Deep Learning Technique.Asian Pacific journal of cancer prevention : APJCP · 2023Article
- Artificial intelligence in diagnostic ultrasonography.Diagnostic and interventional radiology (Ankara, Turkey) · 2023Review
- Fully automatic tumor segmentation of breast ultrasound images with deep learning.Journal of applied clinical medical physics · 2023Article
- Efficient System for Delimitation of Benign and Malignant Breast Masses.Entropy (Basel, Switzerland) · 2022Article
- Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning.Machine learning: science and technology · 2022Article
- Breast lesion detection using an anchor-free network from ultrasound images with segmentation-based enhancement.Scientific reports · 2022Article
- Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI).Journal of digital imaging · 2022Article
- EMT-NET: EFFICIENT MULTITASK NETWORK FOR COMPUTER-AIDED DIAGNOSIS OF BREAST CANCER.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
- Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer.Cancers · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy. The computer-aided detection tool effectively segmented the breast masses, achieving a mean Dice coefficient of 0.82, a true positive fraction (TPF) of 0.84, and a false positive fraction (FPF) of 0.01. By avoiding positioning of an initial seed, the algorithm is able to segment images in real time (13-55 ms per image), and can have potential clinical applications. The algorithm is at par with a conventional seeded algorithm, which had a mean Dice coefficient of 0.84 and performs significantly better (P< 0.0001) than the original U-net algorithm.
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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 Socratic graph.