Jinan Military Region

About: Jinan Military Region is a based out in . It is known for research contribution in the topics: Cancer & Single-nucleotide polymorphism. The organization has 346 authors who have published 243 publications receiving 4541 citations.

Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

Abstract: Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and fine details. Then it adaptively learns to combine these feature maps at each resolution and predict saliency maps with the combined features. Finally, the predicted results are efficiently fused to generate the final saliency map. In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework. By aggregating multi-level convolutional features in this efficient and flexible manner, the proposed saliency model provides accurate salient object labeling. Comprehensive experiments demonstrate that our method performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.

Learning Uncertain Convolutional Features for Accurate Saliency Detection

Abstract: Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection. The key contribution of this work is to learn deep uncertain convolutional features (UCF), which encourage the robustness and accuracy of saliency detection. We achieve this via introducing a reformulated dropout (R-dropout) after specific convolutional layers to construct an uncertain ensemble of internal feature units. In addition, we propose an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in our decoder network. The proposed methods can also be applied to other deep convolutional networks. Compared with existing saliency detection methods, the proposed UCF model is able to incorporate uncertainties for more accurate object boundary inference. Extensive experiments demonstrate that our proposed saliency model performs favorably against state-ofthe-art approaches. The uncertain feature learning mechanism as well as the upsampling method can significantly improve performance on other pixel-wise vision tasks.

A Stagewise Refinement Model for Detecting Salient Objects in Images

Abstract: Deep convolutional neural networks (CNNs) have been successfully applied to a wide variety of problems in computer vision, including salient object detection. To detect and segment salient objects accurately, it is necessary to extract and combine high-level semantic features with low-levelfine details simultaneously. This happens to be a challenge for CNNs as repeated subsampling operations such as pooling and convolution lead to a significant decrease in the initial image resolution, which results in loss of spatial details and finer structures. To remedy this problem, here we propose to augment feedforward neural networks with a novel pyramid pooling module and a multi-stage refinement mechanism for saliency detection. First, our deep feedward net is used to generate a coarse prediction map with much detailed structures lost. Then, refinement nets are integrated with local context information to refine the preceding saliency maps generated in the master branch in a stagewise manner. Further, a pyramid pooling module is applied for different-region-based global context aggregation. Empirical evaluations over six benchmark datasets show that our proposed method compares favorably against the state-of-the-art approaches.

Learning Uncertain Convolutional Features for Accurate Saliency Detection

Abstract: Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection. The key contribution of this work is to learn deep uncertain convolutional features (UCF), which encourage the robustness and accuracy of saliency detection. We achieve this via introducing a reformulated dropout (R-dropout) after specific convolutional layers to construct an uncertain ensemble of internal feature units. In addition, we propose an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in our decoder network. The proposed methods can also be applied to other deep convolutional networks. Compared with existing saliency detection methods, the proposed UCF model is able to incorporate uncertainties for more accurate object boundary inference. Extensive experiments demonstrate that our proposed saliency model performs favorably against state-of-the-art approaches. The uncertain feature learning mechanism as well as the upsampling method can significantly improve performance on other pixel-wise vision tasks.

Tissue-engineered skin containing mesenchymal stem cells improves burn wounds.

Abstract: There is increasing evidence showing that adult stem cells are useful for tissue regeneration. Bone marrow mesenchymal stem cells (MSCs) are self-renewing and are potent in differentiating into multiple cells and tissues. To investigate the practicability of repairing burn wounds with tissue-engineered (TE) skin combined with bone MSCs, we established a burn wound model in the porcine skin. With a controlling temperature and time of the burning device to obtain different degrees of burn wounds, a deep dermal partial thickness burn was introduced to the porcine skin using a heated-brass contact injury at 100 degrees C for 20 s. Collagen-GAG scaffolds were utilized as the matrix; MSCs separated from pigs were seeded on them to form the skin equivalent. When grafted to the burn wounds, the TE skin containing MSCs showed better healing and keratinization, less wound contraction, and more vascularization. Grafts proliferated well and contributed to the neo-tissues. These data suggest that TE skin containing MSCs in a burn defect can accelerate wound healing and receive satisfactory effects.