ArticlePatterns (New York, N.Y.)2023
A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 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
- Clinical evaluation and regression test of a commercial deep-learning auto-segmentation model.Journal of applied clinical medical physics · 2026Article
- A YOLOv8 deep learning model for detecting labral injury via hip magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Deep-learning-based HER2 status assessment from multimodal breast cancer data predicts neoadjuvant therapy response.Nature biomedical engineering · 2026Article
- Spatial-Temporal Information Fusion for Thyroid Nodule Segmentation in Dynamic Contrast-Enhanced MRI: A Novel Approach.Journal of imaging informatics in medicine · 2025Article
- Development of an Automated CAD System for Lesion Detection in DCE-MRI.Journal of imaging informatics in medicine · 2025Article
- Development and validation of an MRI spatiotemporal interaction model for early noninvasive prediction of neoadjuvant chemotherapy response in breast cancer: a multicentre study.EClinicalMedicine · 2025Article
- MHAU-Net: a multi-scale hybrid attention U-shaped network for the segmentation of MRI breast tumors.Quantitative imaging in medicine and surgery · 2025Article
- Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Using a Longitudinally Aware Segmentation Network.Radiology. Artificial intelligence · 2025Article
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.BMC medical imaging · 2025Article
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
- Performance of an AI-powered visualization software platform for precision surgery in breast cancer patients.NPJ breast cancer · 2024Article
- Article
- Radiomics in breast cancer: Current advances and future directions.Cell reports. Medicine · 2024Review
- Spatial and geometric learning for classification of breast tumors from multi-center ultrasound images: a hybrid learning approach.BMC medical imaging · 2024Article
- Evaluating Magnetic Seed Localization in Targeted Axillary Dissection for Node-Positive Early Breast Cancer Patients Receiving Neoadjuvant Systemic Therapy: A Comprehensive Review and Pooled Analysis.Journal of clinical medicine · 2024Review
- A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.Patterns (New York, N.Y.) · 2023Article
Corrections and comments
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Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.
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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 OpenQuestion graph.