ArticleDiagnostics (Basel, Switzerland)2022
Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences.
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 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches.Biomedical engineering online · 2026Pooled it
- Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study.BMC cancer · 2025Article
- Deep learning based on ultrasound images to predict platinum resistance in patients with epithelial ovarian cancer.Biomedical engineering online · 2025Article
- Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study.Journal of medical Internet research · 2025Article
- Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?European radiology experimental · 2024Article
- Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024.Diagnostics (Basel, Switzerland) · 2024Review
- An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images.Journal of imaging informatics in medicine · 2024Article
- Optimal view detection for ultrasound-guided supraclavicular block using deep learning approaches.Scientific reports · 2023Article
- Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Overview of Artificial Intelligence in Breast Cancer Medical Imaging.Journal of clinical medicine · 2023Review
- HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy.Diagnostics (Basel, Switzerland) · 2022Article
- A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis.In vivo (Athens, Greece)Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.
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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.