[HTML][HTML] Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review
The relatively recent reintroduction of deep learning has been a revolutionary force in the
interpretation of diagnostic imaging studies. However, the technology used to acquire those …
interpretation of diagnostic imaging studies. However, the technology used to acquire those …
A comprehensive survey on deep-learning-based breast cancer diagnosis
Simple Summary Breast cancer was diagnosed in 2.3 million women, and around 685,000
deaths from breast cancer were recorded globally in 2020, making it the most common …
deaths from breast cancer were recorded globally in 2020, making it the most common …
Automated breast cancer detection models based on transfer learning
M Alruwaili, W Gouda - Sensors, 2022 - mdpi.com
Breast cancer is among the leading causes of mortality for females across the planet. It is
essential for the well-being of women to develop early detection and diagnosis techniques …
essential for the well-being of women to develop early detection and diagnosis techniques …
[Retracted] Dense Convolutional Neural Network for Detection of Cancer from CT Images
SVN Sreenivasu, S Gomathi, MJ Kumar… - BioMed Research …, 2022 - Wiley Online Library
In this paper, we develop a detection module with strong training testing to develop a dense
convolutional neural network model. The model is designed in such a way that it is trained …
convolutional neural network model. The model is designed in such a way that it is trained …
The role of deep learning in advancing breast cancer detection using different imaging modalities: a systematic review
Simple Summary Breast cancer is the most common cancer, which resulted in the death of
700,000 people around the world in 2020. Various imaging modalities have been utilized to …
700,000 people around the world in 2020. Various imaging modalities have been utilized to …
A competition, benchmark, code, and data for using artificial intelligence to detect lesions in digital breast tomosynthesis
Importance An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in
digital breast tomosynthesis (DBT) could significantly improve detection accuracy and …
digital breast tomosynthesis (DBT) could significantly improve detection accuracy and …
Anatomy of domain shift impact on U-Net layers in MRI segmentation
Abstract Domain Adaptation (DA) methods are widely used in medical image segmentation
tasks to tackle the problem of differently distributed train (source) and test (target) data. We …
tasks to tackle the problem of differently distributed train (source) and test (target) data. We …
Automatic classification of simulated breast tomosynthesis whole images for the presence of microcalcification clusters using deep CNNs
Microcalcification clusters (MCs) are among the most important biomarkers for breast
cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning …
cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning …
Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review
Background Recent advancements in computing power and state-of-the-art algorithms have
helped in more accessible and accurate diagnosis of numerous diseases. In addition, the …
helped in more accessible and accurate diagnosis of numerous diseases. In addition, the …
Applying graph convolution neural network in digital breast tomosynthesis for cancer classification
Digital breast tomosynthesis, or 3D mammography, has advanced the field of breast imaging
diagnosis. It has been rapidly replacing the traditional full-field digital mammography …
diagnosis. It has been rapidly replacing the traditional full-field digital mammography …