Neural Network Design

1. Cross-Modality High-Frequency Transformer for MR Image Super-Resolution

2. Densely Nested Top-Down Flows for Salient Object Detection

3. M3Net: A Multi-scale Multi-view Framework for Multi-phase Pancreas Segmentation Based on Cross-phase Non-local Attention

The complementation of arterial and venous phases visual information of CTs can help better distinguish the pancreas from its surrounding structures. However, the exploration of cross-phase contextual information is still under research in …

4. Deep Transformers for Fast Small Intestine Grounding in Capsule Endoscope Video

Capsule endoscopy is an evolutional technique for examining and diagnosing intractable gastrointestinal diseases. Because of the huge amount of data, analyzing capsule endoscope videos is very time-consuming and labor-intensive for gastrointestinal …

5. PNEN: Pyramid Non-Local Enhanced Networks

Existing neural networks proposed for low-level image processing tasks are usually implemented by stacking convolution layers with limited kernel size. Every convolution layer merely involves in context information from a small local neighborhood. …

6. Self-Enhanced Convolutional Network for Facial Video Hallucination

As a domain-specific super-resolution problem, facial image hallucination has enjoyed a series of breakthroughs thanks to the advances of deep convolutional neural networks. However, the direct migration of existing methods to video is still …

7. Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation

Recently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in com- puter aided diagnosis. This paper aims to address the pancreas segmen- tation task in 3D computed tomography volumes. …