For the breast mass segmentation in whole mammograms, in our studies, we observe that
there is an enormous performance reduction in the case of considering the normal data …

U-Net is a commonly used deep learning model for mammogram segmentation. Despite
outstanding overall performance in segmenting, U-Net still faces from two aspects of …

Mass segmentation in the mammogram is a necessary and challenging task in the computer-
aided diagnosis of breast cancer. Most of the existing methods tend to segment the mass by …

Purpose: Since breast mass is a clear sign of breast cancer, its precise segmentation is of
great significance for the diagnosis of breast cancer. However, the current diagnosis relies …

Breast cancer is currently the second most fatal cancer in women, but timely diagnosis and
treatment can reduce its mortality. Breast masses are the most obvious means of cancer …

Purpose Breast mass segmentation in mammograms remains a crucial yet challenging topic
in computer‐aided diagnosis systems. Existing algorithms mainly used mass‐centered …

Mammography is one of the most commonly applied tools for early breast cancer screening.
Automatic segmentation of breast masses in mammograms is essential but challenging due …

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for
Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems …

Background Breast cancer is one of the most prevalent malignancies diagnosed in women.
Mammogram inspection in the search and delineation of breast tumors is an essential …

Breast mass segmentation in mammograms is still a challenging and clinically valuable task.
In this paper, we propose an effective and lightweight segmentation model based on …