Breast cancer is a significant global health burden, with increasing morbidity and mortality
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …

Simple Summary Breast cancer is the most common cancer, which resulted in the death of
700,000 people around the world in 2020. Various imaging modalities have been utilized to …

Breast tumor segmentation and recognition from mammograms play a key role in healthcare
and treatment services. As different tumors in mammography have dissimilar densities …

Background Deep learning models require large‐scale training to perform confidently, but
obtaining annotated datasets in medical imaging is challenging. Weak annotation has …

Simple Summary Quantitative image analysis of cancers requires accurate tumor
segmentation that is often performed manually. In this study, we developed a deep learning …

Purpose The aim of this study was to develop a convolutional neural network (CNN) for the
automatic detection and segmentation of gliomas using [18F] fluoroethyl-L-tyrosine ([18F] …

Breast cancer (BC) is a multifaceted genetic malignancy that accounts for the majority of
cancer fatalities in women. Dynamic Contrast-Enhanced Magnetic Resonance Imaging …

One of the most cautious diseases that produced an increased death rate around the world
is breast cancer. The early detection of this disease can save the lives of people. Therefore …

Brain tumor imaging has been part of the clinical routine for many years to perform non-
invasive detection and grading of tumors. Tumor segmentation is a crucial step for managing …

Fully supervised medical image segmentation methods use pixel-level labels to achieve
good results, but obtaining such large-scale, high-quality labels is cumbersome and time …