Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image
segmentation processes. Thus, the precise segmentation of organs and their lesions may …

Introduction Due to the complex anatomy of the spine and the intricate surgical procedures
involved, spinal surgery demands a high level of technical expertise from surgeons. The …

While supervised learning has achieved remarkable success, obtaining large-scale labeled
datasets in biomedical imaging is often impractical due to high costs and the time …

The proliferation of medical imaging in clinical diagnostics has led to an overwhelming
volume of image data, presenting a challenge for efficient storage, management, and …

Colorectal cancer is a common malignant tumour of the gastrointestinal tract. Studies have
shown that colonoscopy can be an effective screening method for detecting colon polyps …

Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause
facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the …

Accurate segmentation of organs and lesions from medical images holds paramount
importance in aiding physicians with diagnosis and monitor diseases. At present, the …

Convolutional Neural Networks (CNNs) and Transformer-based self-attention models have
become standard for medical image segmentation. This paper demonstrates that …

Semantic segmentation neural network classifies each pixel of the input image with
semantic labels and it is widely used in varying domains such as remote sensing …

Objective. Automatic medical image segmentation is crucial for accurately isolating target
tissue areas in the image from background tissues, facilitating precise diagnoses and …