Position Prior Attention Network for Pancreas Tumor Segmentation.

Segmentation of pancreatic tumors on CT images is essential for the diagnosis and treatment of pancreatic cancer. However, low contrast between the pancreas and the tumor, as well as variable tumor shape and position, makes segmentation challenging. To solve the problem, we propose a Position Prior Attention Network (PPANet) with a pseudo segmentation generation module (PSGM) and a position prior attention module (PPAM).

Dual-Attention Model Fusing CNN and Transformer for Pancreas Segmentation.

Pancreatic cancer is a highly malignant cancer of the digestive tract and is rapidly progressing and spreading clinically. Automatic and accurate pancreatic tissue segmentation in abdominal CT images is essential for the early diagnosis of pancreatic-related diseases. It is challenging that the pancreas is small in size and complex in morphology.

Prediction of New-Onset Diabetes after Pancreatectomy with Subspace Clustering based Multi-view Feature Selection.

The pancreas plays an important role in glucose metabolism, and developing diabetes or long-term glucose metabolism disturbance may be a prevalent sequela after pancreatectomy. Nevertheless, relative factors of new-onset diabetes after pancreatectomy stay unclear. Radiomics analysis is potential to identify image markers for disease prediction or prognosis.