[HTML][HTML] Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review

J Bai, R Posner, T Wang, C Yang, S Nabavi - Medical image analysis, 2021 - Elsevier
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 …

Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review

S Hussain, Y Lafarga-Osuna, M Ali, U Naseem… - BMC …, 2023 - Springer
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 …

New convolutional neural network model for screening and diagnosis of mammograms

C Zhang, J Zhao, J Niu, D Li - PLoS One, 2020 - journals.plos.org
Breast cancer is the most common cancer in women and poses a great threat to women's life
and health. Mammography is an effective method for the diagnosis of breast cancer, but the …

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

FJ Pérez-Benito, F Signol, JC Perez-Cortes… - Computer Methods and …, 2020 - Elsevier
Abstract Background and Objective Breast cancer is the most frequent cancer in women.
The Spanish healthcare network established population-based screening programs in all …

Healthcare data heterogeneity and its contribution to machine learning performance

FJ Pérez Benito - 2020 - riunet.upv.es
[EN] The data quality assessment has many dimensions, from those so obvious as the data
completeness and consistency to other less evident such as the correctness or the ability to …