Recent improvements in biomedical image analysis using deep learning based neural
networks could be exploited to enhance the performance of Computer Aided Diagnosis …

Breast cancer's high mortality rate is often linked to late diagnosis, with mammograms as key
but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed …

The paper presents quantitative results of a preliminary study undertaken as part of Decision
Support and Information Management System for Breast Cancer (DESIREE). DESIREE is a …

Breast cancer is the most commonly diagnosed female malignancy worldwide. Recent
developments in deep convolutional neural networks have shown promising performance …

Breast density is considered to be one of the major risk factors in developing breast cancer.
High breast density can also affect the accuracy of mammographic abnormality detection …

Breast cancer, the most prevalent malignant tumor among women, poses a significant threat
to patients' physical and mental well-being. Recent advances in early screening technology …

Background High-throughput phenotyping based on non-destructive imaging has great
potential in plant biology and breeding programs. However, efficient feature extraction and …

Breast cancer has a high incidence among women worldwide. This, together with the recent
developments in deep learning based convolutional networks, have motivated research …

Breast cancer is considered to have a high incidence among women worldwide. Recent
development in biomedical image analysis using deep learning based neural networks …

Current deep learning based detection models tackle detection and segmentation tasks by
casting them to pixel or patch-wise classification. To automate the initial mass lesion …