ArticleAnnals of biomedical engineering2018
Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms.
Article in Annals of biomedical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis.Clinical and experimental medicine · 2023Pooled it
- Predictive deep learning model based on contrast-enhanced mammography for breast cancer diagnosis: a pilot study.BJR artificial intelligence · 2026Article
- Article
- A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.Diagnostics (Basel, Switzerland) · 2024Article
- AI-Based Cancer Detection Model for Contrast-Enhanced Mammography.Bioengineering (Basel, Switzerland) · 2023Article
- Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study.European radiology · 2023Article
- Radiomic analysis in contrast-enhanced mammography using a multivendor data set: accuracy of models according to segmentation techniques.The British journal of radiology · 2023Article
- Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography.Diagnostics (Basel, Switzerland) · 2022Article
- Review
- Diagnostic Accuracy of Machine Learning Models on Mammography in Breast Cancer Classification: A Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Article
- Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours.European radiology · 2022Article
- A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.Bioengineering (Basel, Switzerland) · 2022Article
- Identifying factors that may influence the classification performance of radiomics models using contrast-enhanced mammography (CEM) images.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions.European radiology · 2022Article
- Developing new quantitative CT image markers to predict prognosis of acute ischemic stroke patients.Journal of X-ray science and technology · 2022Article
- Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study.Quantitative imaging in medicine and surgery · 2021Article
- Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.IEEE transactions on bio-medical engineering · 2021Article
- Multiview multimodal network for breast cancer diagnosis in contrast-enhanced spectral mammography images.International journal of computer assisted radiology and surgery · 2021Article
- Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images.Computer methods and programs in biomedicine · 2021Article
- Radiomic Analysis of Contrast-Enhanced Mammography With Different Image Types: Classification of Breast Lesions.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Contrast-enhanced digital mammography (CEDM) is a promising imaging modality in breast cancer diagnosis. This study aims to investigate how to optimally develop a computer-aided diagnosis (CAD) scheme of CEDM images to classify breast masses. A CEDM dataset of 111 patients was assembled, which includes 33 benign and 78 malignant cases. Each CEDM includes two types of images namely, low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting on LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron based machine learning classifiers that integrate with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. When applying CAD to DES and LE images with original segmentation, areas under ROC curves (AUC) were 0.759 ± 0.053 and 0.753 ± 0.047, respectively. After mapping the mass regions optimally segmented on DES images to LE images, AUC significantly increased to 0.848 ± 0.038 (p < 0.01). Study demonstrated that DES images eliminated overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. The study demonstrated that applying a novel approach to optimally map mass region segmented from DES images to LE images enabled CAD to yield significantly improved performance.
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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.