Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …
AI applications to medical images: From machine learning to deep learning
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …
research and healthcare services. This review focuses on challenges points to be clarified …
[HTML][HTML] Application of deep learning in breast cancer imaging
This review gives an overview of the current state of deep learning research in breast cancer
imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as …
imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as …
Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
MR Chetan, FV Gleeson - European radiology, 2021 - Springer
Objectives Radiomics is the extraction of quantitative data from medical imaging, which has
the potential to characterise tumour phenotype. The radiomics approach has the capacity to …
the potential to characterise tumour phenotype. The radiomics approach has the capacity to …
A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI
Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve
radiologists' performance in the clinical diagnosis of breast cancer. This machine learning …
radiologists' performance in the clinical diagnosis of breast cancer. This machine learning …
Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement
Objectives To evaluate radiomics studies according to radiomics quality score (RQS) and
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or …
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or …
Novel approaches to screening for breast cancer
Screening for breast cancer reduces breast cancer–related mortality and earlier detection
facilitates less aggressive treatment. Unfortunately, current screening modalities are …
facilitates less aggressive treatment. Unfortunately, current screening modalities are …
Diagnosis of benign and malignant breast lesions on DCE‐MRI by using radiomics and deep learning with consideration of peritumor tissue
J Zhou, Y Zhang, KT Chang, KE Lee… - Journal of Magnetic …, 2020 - Wiley Online Library
Background Computer‐aided methods have been widely applied to diagnose lesions
detected on breast MRI, but fully‐automatic diagnosis using deep learning is rarely reported …
detected on breast MRI, but fully‐automatic diagnosis using deep learning is rarely reported …
Semi-supervised GAN-based radiomics model for data augmentation in breast ultrasound mass classification
Abstract Background and Objective The capability of deep learning radiomics (DLR) to
extract high-level medical imaging features has promoted the use of computer-aided …
extract high-level medical imaging features has promoted the use of computer-aided …