Laplacian-guided hierarchical transformer: A network for medical image segmentation.

Background And Objective: Accurate medical image segmentation is crucial for diagnosis and treatment planning, particularly in tumor localization and organ measurement. Despite the success of Transformer models in various domains, they still struggle to capture high-frequency features, limiting their performance in medical image segmentation, especially in edge texture extraction. To overcome this limitation and improve segmentation accuracy, this study proposes a novel model architecture aimed at enhancing the Transformer's ability to capture and integrate both high-frequency and low-frequency features.

Material extrusion 3D-printing technology: A new strategy for constructing water-soluble, high-dose, sustained-release drug formulations.

The advantage of low-temperature forming through direct ink writing (DIW) 3D printing is becoming a strategy for the construction of innovative drug delivery systems (DDSs). Optimization of the complex formulation, including factors such as the printing ink, presence of solvents, and potential low mechanical strength, are challenges during process development. This study presents an application of DIW to fabricate water-soluble, high-dose, and sustained-release DDSs.

An Efficient and Rapid Medical Image Segmentation Network.

Accurate medical image segmentation is an essential part of the medical image analysis process that provides detailed quantitative metrics. In recent years, extensions of classical networks such as UNet have achieved state-of-the-art performance on medical image segmentation tasks. However, the high model complexity of these networks limits their applicability to devices with constrained computational resources.

Extension-contraction transformation network for pancreas segmentation in abdominal CT scans.

Accurate and automatic pancreas segmentation from abdominal computed tomography (CT) scans is crucial for the diagnosis and prognosis of pancreatic diseases. However, the pancreas accounts for a relatively small portion of the scan and presents high anatomical variability and low contrast, making traditional automated segmentation methods fail to generate satisfactory results. In this paper, we propose an extension-contraction transformation network (ECTN) and deploy it into a cascaded two-stage segmentation framework for accurate pancreas segmenting.

Classification of Alzheimer's disease in MRI images using knowledge distillation framework: an investigation.

Purpose: Computer-aided MRI analysis is helpful for early detection of Alzheimer's disease(AD). Recently, 3D convolutional neural networks(CNN) are widely used to analyse MRI images. However, 3D CNN requires huge memory cost.

A metric learning-based method using graph neural network for pancreatic cystic neoplasm classification from CTs.

Purpose: Pancreatic cystic neoplasms (PCNs) are relatively rare neoplasms and difficult to be classified preoperatively. Ordinary deep learning methods have great potential to provide support for doctors in PCNs classification but require a quantity of labeled samples and exact segmentation of neoplasm. The proposed metric learning-based method using graph neural network (GNN) aims to overcome the limitations brought by small and imbalanced dataset and get fast and accurate PCNs classification result from computed tomography (CT) images.

Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN.

Purpose: Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Radiologists usually review knee X-ray images and grade the severity of the impairments according to the Kellgren-Lawrence grading scheme. However, this approach becomes inefficient in hospitals with high throughput as it is time-consuming, tedious and also subjective.