Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
EH Houssein, MM Emam, AA Ali… - Expert Systems with …, 2021 - Elsevier
Breast cancer is the second leading cause of death for women, so accurate early detection
can help decrease breast cancer mortality rates. Computer-aided detection allows …
can help decrease breast cancer mortality rates. Computer-aided detection allows …
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …
units are used to extract patterns from data in order to solve complex problems. Deep …
[HTML][HTML] Computer-aided breast cancer detection and classification in mammography: A comprehensive review
Cancer is the second cause of mortality worldwide and it has been identified as a perilous
disease. Breast cancer accounts for∼ 20% of all new cancer cases worldwide, making it a …
disease. Breast cancer accounts for∼ 20% of all new cancer cases worldwide, making it a …
Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives
Although computer-aided diagnosis (CAD) is widely used in mammography, conventional
CAD programs that use prompts to indicate potential cancers on the mammograms have not …
CAD programs that use prompts to indicate potential cancers on the mammograms have not …
Deep learning in mammography and breast histology, an overview and future trends
A Hamidinekoo, E Denton, A Rampun, K Honnor… - Medical image …, 2018 - Elsevier
Recent improvements in biomedical image analysis using deep learning based neural
networks could be exploited to enhance the performance of Computer Aided Diagnosis …
networks could be exploited to enhance the performance of Computer Aided Diagnosis …
A deep learning approach for the analysis of masses in mammograms with minimal user intervention
We present an integrated methodology for detecting, segmenting and classifying breast
masses from mammograms with minimal user intervention. This is a long standing problem …
masses from mammograms with minimal user intervention. This is a long standing problem …
Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome
JPB O'Connor, CJ Rose, JC Waterton… - Clinical Cancer …, 2015 - AACR
Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance
and may influence response to therapy. Imaging can quantify the spatial variation in …
and may influence response to therapy. Imaging can quantify the spatial variation in …
Benign and malignant breast tumors classification based on region growing and CNN segmentation
Breast cancer is regarded as one of the most frequent mortality causes among women. As
early detection of breast cancer increases the survival chance, creation of a system to …
early detection of breast cancer increases the survival chance, creation of a system to …
Inbreast: toward a full-field digital mammographic database
IC Moreira, I Amaral, I Domingues, A Cardoso… - Academic radiology, 2012 - Elsevier
RATIONALE AND OBJECTIVES: Computer-aided detection and diagnosis (CAD) systems
have been developed in the past two decades to assist radiologists in the detection and …
have been developed in the past two decades to assist radiologists in the detection and …
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Medical images differ from natural images in significantly higher resolutions and smaller
regions of interest. Because of these differences, neural network architectures that work well …
regions of interest. Because of these differences, neural network architectures that work well …