Research in the medical imaging field using deep learning approaches has become
progressively contingent. Scientific findings reveal that supervised deep learning methods' …

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 …

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 …

Convolutional neural network (CNN) has promoted the development of diagnosis
technology of medical images. However, the performance of CNN is limited by insufficient …

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the
deployment and adoption of AI technologies remain limited in real-world clinical practice. In …

Convolutional Neural Networks (CNN) have received a large share of research in
mammography image analysis due to their capability of extracting hierarchical features …

Computer-aided detection systems based on deep learning have shown good performance
in breast cancer detection. However, high-density breasts show poorer detection …

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 …

Cross-silo Federated learning (FL) has become a promising tool in machine learning
applications for healthcare. It allows hospitals/institutions to train models with sufficient data …

Early detection of breast cancer in mammography screening via deep-learning based
computer-aided detection systems shows promising potential in improving the curability and …