Projects

Background Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy.

Accurate and reliable segmentation of colorectal tumors and surrounding colorectal tissues on 3D magnetic resonance images has critical importance in preoperative prediction, staging, and radiotherapy. Previous works simply combine multilevel features without aggregating representative semantic information and without compensating for the loss of spatial information caused by down-sampling.

This paper proposes a local region-based variational region growing algorithm, which integrates local region and shape prior to segment the mandible accurately. Firstly, we select initial seeds in the CBCT image and then calculate candidate point sets and the local region energy function of each point.

Detecting neurofibrillary tangles is an important procedure in the assessment of the intensity and distribution pattern of hippocampal tau pathology, which are the principal clinical phenotypes associated with Alzheimer’s disease. Existing deep learning based detectors still face a critical obstacle: the difficulty in detecting extremely small objects in high resolution images.

Point cloud data (PCD) can record a high-resolution model of the environment much more efficiently than the conventional geometrical mesh. This feature makes PCD suitable for the radio channel prediction of a new environment, especially for the millimeter-wave (mmWave).

For various reasons, such as new requirements, architecture refactoring, and bug fixing, software projects often evolve to yield better quality and performance. All changes produced during the development process are reflected in the source code, which provides an opportunity to explore software evolution.

Feature classification has always been one of the research hotspots in scientific visualization.However, conventional interactive feature classification methods rely on prior knowledge and typicallyrequire trial and error, whereas feature classification based on data mining is generally based on localfeatures; therefore, obtaining good results with traditional methods is difficult.

Dynamic networks evolve with their structures changing over time. It is still a challenging problem to efficiently explore the evolution of dynamic networks in terms of both their structural and temporal properties.

This work introduces a new method for analyzing the scattering from combined conductor-dielectric bodies based on equivalent principle algorithm (EPA). The object is decomposed into several different parts. In each part, it can be an inhomogeneous dielectric or a perfect electric conductor (PEC) of arbitrary shape.

A feasible approach to optimize the characteristics of electromagnetic (EM) objects through shape deformation is proposed. We provide a complete and detailed procedure of the optimization and investigate the shape deformation formulations for some typical EM objects.