Cargando…
Tensor-Based Learning for Detecting Abnormalities on Digital Mammograms
In this study, we propose a tensor-based learning model to efficiently detect abnormalities on digital mammograms. Due to the fact that the availability of medical data is limited and often restricted by GDPR (general data protection regulation) compliance, the need for more sophisticated and less d...
Autores principales: | Tzortzis, Ioannis N., Davradou, Agapi, Rallis, Ioannis, Kaselimi, Maria, Makantasis, Konstantinos, Doulamis, Anastasios, Doulamis, Nikolaos |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601228/ https://www.ncbi.nlm.nih.gov/pubmed/36292078 http://dx.doi.org/10.3390/diagnostics12102389 |
Ejemplares similares
-
COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level
por: Kavouras, Ioannis, et al.
Publicado: (2022) -
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring
por: Sykiotis, Stavros, et al.
Publicado: (2022) -
A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring
por: Kaselimi, Maria, et al.
Publicado: (2022) -
Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring
por: Kaselimi, Maria, et al.
Publicado: (2022) -
Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM
por: Bakalos, Nikolaos, et al.
Publicado: (2021)