Considering breast density for the classification of benign and malignant mammograms
ML Huang, TY Lin - Biomedical Signal Processing and Control, 2021 - Elsevier
Background and objective Mammography plays a crucial role in breast cancer screening
because it can be used to diagnose a breast mass and breast calcification region early …
because it can be used to diagnose a breast mass and breast calcification region early …
Cross-view relation networks for mammogram mass detection
In medical image analysis, multi-view modeling is crucial for pathology detection when the
target lesion is presented in different views, eg mass lesions in breasts. Currently …
target lesion is presented in different views, eg mass lesions in breasts. Currently …
Multi‐scale attention‐based convolutional neural network for classification of breast masses in mammograms
J Niu, H Li, C Zhang, D Li - Medical physics, 2021 - Wiley Online Library
Purpose Breast cancer is the cancer with the highest incidence in women, and early
detection can effectively improve the survival rate of patients. Mammography is an important …
detection can effectively improve the survival rate of patients. Mammography is an important …
Breast tissue segmentation and mammographic risk scoring using deep learning
Mammographic scoring of density and texture are established methods to relate to the risk of
breast cancer. We present a method that learns descriptive features from unlabeled …
breast cancer. We present a method that learns descriptive features from unlabeled …
Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning
Breast mass detection is a challenging task in mammogram, since mass is usually
embedded and surrounded by various normal tissues with similar density. Recently, deep …
embedded and surrounded by various normal tissues with similar density. Recently, deep …
Connected-UNets: a deep learning architecture for breast mass segmentation
Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious
breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic …
breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic …
Multi-feature deep information bottleneck network for breast cancer classification in contrast enhanced spectral mammography
There is considerable variation in the size, shape and location of tumours, which makes it
challenging for radiologists to diagnose breast cancer. Automated diagnosis of breast …
challenging for radiologists to diagnose breast cancer. Automated diagnosis of breast …
Residual deep learning system for mass segmentation and classification in mammography
D Abdelhafiz, S Nabavi, R Ammar, C Yang… - Proceedings of the 10th …, 2019 - dl.acm.org
Automatic extraction of breast mass in mammogram (MG) images is a challenging task due
to the varying sizes, shapes, and textures of masses. Moreover, the density of MGs makes …
to the varying sizes, shapes, and textures of masses. Moreover, the density of MGs makes …
Classification of breast mass in two‐view mammograms via deep learning
H Li, J Niu, D Li, C Zhang - IET Image Processing, 2021 - Wiley Online Library
Breast cancer is the second deadliest cancer among women. Mammography is an important
method for physicians to diagnose breast cancer. The main purpose of this study is to use …
method for physicians to diagnose breast cancer. The main purpose of this study is to use …
Adversarial deep structured nets for mass segmentation from mammograms
Mass segmentation provides effective morphological features which are important for mass
diagnosis. In this work, we propose a novel end-to-end network for mammographic mass …
diagnosis. In this work, we propose a novel end-to-end network for mammographic mass …