Linda Blahová
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Linda Blahová has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Jozef Kostolny
Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
2 shared publications - Anna Birková
Department of Medical and Clinical Biochemistry, Pavol Jozef Šafárik University, 040 11 Košice, Slovakia
1 shared publication - Ivan Cimrák
Biomedical Modelling and Computations Group, Faculty of Management Science and Informatics, University of Žilina, Žilina, Slovakia
1 shared publication - Katarína Dubayová
Department of Medical and Clinical Biochemistry, Faculty of Medicine, P. J. Šafárik University, Košice, Slovakia
1 shared publication - Monika Švecová
Department of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Šafárik University, Košice, Slovakia
1 shared publication Mária Mareková
Department of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Tr. SNP 1, 040 01, Košice, Slovakia
1 shared publication
Latest Publications
- Open Access
- 24 February 2025
- Published by MDPI AG in Bioengineering
- Vol. 12 (3) , 232
Neural Network-Based Mammography Analysis: Augmentation Techniques for Enhanced Cancer Diagnosis—A Review
Application of machine learning techniques in breast cancer detection has significantly advanced due to the availability of annotated mammography datasets. This paper provides a review of mammography studies using key datasets such as CBIS-DDSM, VinDr-Mammo, and CSAW-CC, which play a critical role in training classification and detection models. The analysis of the studies produces a set of data augmentation techniques in mammography, and their impact and performance improvements in detecting abnormalities in breast tissue are studied. The study discusses the challenges of dataset imbalances and presents methods to address this issue, like synthetic data generation and GAN augmentation as potential solutions. The work underscores the importance of dataset design dedicated for experiments, detailed annotations, and the usage of machine learning models and architectures in improving breast cancer screening models, with a focus on BI-RADS classification. Future directions include refining augmentation methods, addressing class imbalance, and enhancing model interpretability through tools like Grad-CAM.
- 24 February 2025
- Published by MDPI AG in Bioengineering
- Vol. 12 (3) , 232
Open AccessNeural Network-Based Mammography Analysis: Augmentation Techniques for Enhanced Cancer Diagnosis—A Review
Application of machine learning techniques in breast cancer detection has significantly advanced due to the availability of annotated mammography datasets. This paper provides a review of mammography studies using key datasets such as CBIS-DDSM, VinDr-Mammo, and CSAW-CC, which play a critical role in training classification and detection models. The analysis of the studies produces a set of data augmentation techniques in mammography, and their impact and performance improvements in detecting abnormalities in breast tissue are studied. The study discusses the challenges of dataset imbalances and presents methods to address this issue, like synthetic data generation and GAN augmentation as potential solutions. The work underscores the importance of dataset design dedicated for experiments, detailed annotations, and the usage of machine learning models and architectures in improving breast cancer screening models, with a focus on BI-RADS classification. Future directions include refining augmentation methods, addressing class imbalance, and enhancing model interpretability through tools like Grad-CAM.