Hierarchical convolutional neural networks for segmentation of breast tumors in MRI with application to radiogenomics
Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance
imaging (DCE-MRI) is a challenging problem and an active area of research. Particular …
imaging (DCE-MRI) is a challenging problem and an active area of research. Particular …
Current status and future perspectives of artificial intelligence in magnetic resonance breast imaging
Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many
scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well …
scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well …
Three-dimensional affinity learning based multi-branch ensemble network for breast tumor segmentation in MRI
Accurate and automatic breast tumor segmentation based on dynamic contrast-
enhancement magnetic resonance imaging (DCE-MRI) plays an important role in breast …
enhancement magnetic resonance imaging (DCE-MRI) plays an important role in breast …
Training medical image analysis systems like radiologists
The training of medical image analysis systems using machine learning approaches follows
a common script: collect and annotate a large dataset, train the classifier on the training set …
a common script: collect and annotate a large dataset, train the classifier on the training set …
Automatic breast lesion detection in ultrafast DCE‐MRI using deep learning
Purpose We propose a deep learning‐based computer‐aided detection (CADe) method to
detect breast lesions in ultrafast DCE‐MRI sequences. This method uses both the 3D spatial …
detect breast lesions in ultrafast DCE‐MRI sequences. This method uses both the 3D spatial …
Mixed 2D and 3D convolutional network with multi-scale context for lesion segmentation in breast DCE-MRI
H Wang, J Cao, J Feng, Y Xie, D Yang… - … Signal Processing and …, 2021 - Elsevier
Background Breast lesion segmentation in dynamic contrast-enhanced magnetic resonance
imaging (DCE-MRI) is an essential step for breast cancer analysis. 2D networks have the …
imaging (DCE-MRI) is an essential step for breast cancer analysis. 2D networks have the …
An end-to-end deep learning histochemical scoring system for breast cancer TMA
One of the methods for stratifying different molecular classes of breast cancer is the
Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain …
Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain …
Three-dimensional breast tumor segmentation on DCE-MRI with a multilabel attention-guided joint-phase-learning network
Accurate breast and tumor segmentations from dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI) is vital in breast disease diagnosis. Here, we propose a …
resonance imaging (DCE-MRI) is vital in breast disease diagnosis. Here, we propose a …
Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results
VS Parekh, KJ Macura, SC Harvey, IR Kamel… - Medical …, 2020 - Wiley Online Library
Purpose Deep learning is emerging in radiology due to the increased computational
capabilities available to reading rooms. These computational developments have the ability …
capabilities available to reading rooms. These computational developments have the ability …
MRI‐based breast cancer classification and localization by multiparametric feature extraction and combination using deep learning
C Cong, X Li, C Zhang, J Zhang, K Sun… - Journal of Magnetic …, 2024 - Wiley Online Library
Background Deep learning (DL) have been reported feasible in breast MRI. However, the
effectiveness of DL method in mpMRI combinations for breast cancer detection has not been …
effectiveness of DL method in mpMRI combinations for breast cancer detection has not been …