Learning-based infrared small object detection methods currently rely heavily on the
classification backbone network. This tends to result in tiny object loss and feature …

Abstract Segmentation of abdominal Computed Tomography (CT) scan is essential for
analyzing, diagnosing, and treating visceral organ diseases (eg, hepatocellular carcinoma) …

Convolutional neural networks (CNNs) have made enormous progress in medical image
segmentation. The learning of CNNs is dependent on a large amount of training data with …

Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and
accurate treatment mainly rely on medical imaging, so accurate medical image analysis is …

Abdominal organs play a significant role in regulating various functional systems. Any
impairment in its functioning can lead to cancerous diseases. Diagnosing these diseases …

Accurate and efficient pancreas segmentation is the basis for subsequent diagnosis and
qualitative treatment of pancreatic cancer. Segmenting the pancreas from abdominal CT …

Pancreatic cancer is the most difficult-to-detect cancer with the highest fatality rate. Pancreas
analysis through abdominal computed tomography (CT) is challenging because the …

Accurate pancreas segmentation is crucial for the diagnostic assessment of pancreatic
cancer. However, large position changes, high variability in shape and size, and the …

Medical image segmentation is a fundamental research problem in the field of medical
image processing. Recently, the Transformer have achieved highly competitive performance …

Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures,
and predicting patient prognosis. Therefore, various studies have designed segmentation …