[HTML][HTML] Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects

O Díaz, A Rodríguez-Ruíz, I Sechopoulos - European journal of radiology, 2024‏ - Elsevier
Purpose This review provides an overview of the current state of artificial intelligence (AI)
technology for automated detection of breast cancer in digital mammography (DM) and …

[HTML][HTML] Image augmentation techniques for mammogram analysis

P Oza, P Sharma, S Patel, F Adedoyin, A Bruno - Journal of Imaging, 2022‏ - mdpi.com
Research in the medical imaging field using deep learning approaches has become
progressively contingent. Scientific findings reveal that supervised deep learning methods' …

Vision-transformer-based transfer learning for mammogram classification

G Ayana, K Dese, Y Dereje, Y Kebede, H Barki… - Diagnostics, 2023‏ - mdpi.com
Breast mass identification is a crucial procedure during mammogram-based early breast
cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or …

DM-CNN: Dynamic Multi-scale Convolutional Neural Network with uncertainty quantification for medical image classification

Q Han, X Qian, H Xu, K Wu, L Meng, Z Qiu… - Computers in biology …, 2024‏ - Elsevier
Convolutional neural network (CNN) has promoted the development of diagnosis
technology of medical images. However, the performance of CNN is limited by insufficient …

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

K Lekadir, AF Frangi, AR Porras, B Glocker, C Cintas… - bmj, 2025‏ - bmj.com
Despite major advances in artificial intelligence (AI) research for healthcare, the deployment
and adoption of AI technologies remain limited in clinical practice. This paper describes the …

Convolutional networks and transformers for mammography classification: an experimental study

M Cantone, C Marrocco, F Tortorella, A Bria - Sensors, 2023‏ - mdpi.com
Convolutional Neural Networks (CNN) have received a large share of research in
mammography image analysis due to their capability of extracting hierarchical features …

[HTML][HTML] A systematic review of generalization research in medical image classification

S Matta, M Lamard, P Zhang, A Le Guilcher… - Computers in biology …, 2024‏ - Elsevier
Abstract Numerous Deep Learning (DL) classification models have been developed for a
large spectrum of medical image analysis applications, which promises to reshape various …

Domain generalization for mammographic image analysis with contrastive learning

Z Li, Z Cui, L Zhang, S Wang, C Lei, X Ouyang… - Computers in Biology …, 2025‏ - Elsevier
The deep learning technique has been shown to be effectively addressed several image
analysis tasks in the computer-aided diagnosis scheme for mammography. The training of …

Vision transformers in domain adaptation and domain generalization: a study of robustness

S Alijani, J Fayyad, H Najjaran - Neural Computing and Applications, 2024‏ - Springer
Deep learning models are often evaluated in scenarios where the data distribution is
different from those used in the training and validation phases. The discrepancy presents a …

Mass segmentation and classification from film mammograms using cascaded deep transfer learning

VM Tiryaki - Biomedical Signal Processing and Control, 2023‏ - Elsevier
Breast cancer is the most common type of cancer among women worldwide. Early breast
cancers have a high chance of cure so early diagnosis is critical. Mammography screening …