In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard
and important tasks for several applications in the field of medical analysis. As each brain …

Tumor classification and segmentation are two important tasks for computer-aided diagnosis
(CAD) using 3D automated breast ultrasound (ABUS) images. However, they are …

Medical imaging is a key component in clinical diagnosis, treatment planning and clinical
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …

Breast tumor segmentation from ultrasound images is one of the key steps that help us
characterize and localize tumor regions. However, variable tumor morphology, blurred …

The apical four-chamber (A4C) view in fetal echocardiography is a prenatal examination
widely used for the early diagnosis of congenital heart disease (CHD). Accurate …

We present our novel deep multi-task learning method for medical image segmentation.
Existing multi-task methods demand ground truth annotations for both the primary and …

Breast cancer is the most common cancer in women. Ultrasound is a widely used screening
tool for its portability and easy operation, and DCE-MRI can highlight the lesions more …

U-shaped networks have become prevalent in various medical image tasks such as
segmentation, and restoration. However, most existing U-shaped networks rely on …

Breast ultrasound medical images are characterized by poor imaging quality and irregular
target edges. During the diagnosis process, it is difficult for physicians to segment tumors …