Background Neuroimaging segmentation is increasingly important for diagnosing and
planning treatments for neurological diseases. Manual segmentation is time-consuming …

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with
applications in disease diagnostics, intervention, and treatment planning. Accurate MRI …

Medical image segmentation is a crucial task in many clinical applications, such as tumor
detection and surgical planning. However, the annotation process for medical images is …

Glioma survival risk prediction is of great significance for the individualized treatment and
assessment programs. Currently, most deep learning based survival prediction paradigms …

Brain tumor segmentation (BTS) from magnetic resonance imaging (MRI) scans is crucial for
the diagnosis, treatment planning, and monitoring of therapeutic results. Thus, this research …

As traditional semantic segmentation methods evolve, they typically rely on closed-set
training processes, limiting them to recognize only the classes they were trained on. To …

In recent years, the surge in glioma brain tumor cases has positioned it as the 10th most
prevalent tumor affecting individuals across diverse age groups. Gliomas, characterized by …

Breast cancer, with its high incidence and mortality globally, necessitates early prediction of
local and distant recurrence to improve treatment outcomes. This study develops and …

Honeycomb lung is a radiological manifestation of various lung diseases, seriously
threatening patients' lives worldwide. In clinical practice, the precise localization of lesions …

ABSTRACT Background and Objective Malignant primary brain tumors cause the greatest
number of years of life lost than any other cancer. Grade 4 glioma is particularly devastating …