Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors. Recently, brain tumor segmentation methods based on the symmetric U-Net architecture have achieved favorable performance. Meanwhile, the effectiveness of enhancing local responses for feature extraction and restoration has also been shown in recent works, which may encourage the better performance of the brain tumor segmentation problem. Inspired by this, we try to introduce the attention mechanism into the existing U-Net architecture to explore the effects of local important responses on this task. More specifically, we propose an end-to-end 2D brain tumor segmentation network, i.e., attention residual U-Net (AResU-Net), which simultaneously embeds attention mechanism and residual units into U-Net for the further performance improvement of brain tumor segmentation. AResU-Net adds a series of attention units among corresponding down-sampling and up-sampling processes, and it adaptively rescales features to effectively enhance local responses of down-sampling residual features utilized for the feature recovery of the following up-sampling process. We extensively evaluate AResU-Net on two MRI brain tumor segmentation benchmarks of BraTS 2017 and BraTS 2018 datasets. Experiment results illustrate that the proposed AResU-Net outperforms its baselines and achieves comparable performance with typical brain tumor segmentation methods.

Researches on breast cancer histopathological image classification have achieved a great breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent years. However, due to the inductive bias of locality, CNNs are unable to effectively extract the global feature information of breast cancer histopathological images, limiting the improvement of the classification results. To overcome this shortcoming, this paper reasonably introduces an extra backbone stream of a pure transformer, which consists of a self-attention mechanism to capture global receptive fields of histopathological images, thereby compensating the locality characteristic of CNNs backbone. Based on two backbone streams of CNN and transformer, a dual-stream network called DCET-Net is proposed, which considers local features and global ones simultaneously, and progressively combines them from these two streams to form the final representations for classification. DCET-Net is extensively evaluated on the representative BreakHis histopathological image dataset, and experimental results demonstrate that it is highly competitive with the state-of-the-art CNN methods in breast cancer histopathological image classification task.

Background: Glioma is one of the most common and aggressive primary brain tumors that endanger human health. Tumors segmentation is a key step in assisting the diagnosis and treatment of cancer disease. However, it is a relatively challenging task to precisely segment tumors considering characteristics of brain tumors and the device noise. Recently, with the breakthrough development of deep learning, brain tumor segmentation methods based on fully convolutional neural network (FCN) have illuminated brilliant performance and attracted more and more attention. Methods: In this work, we propose a novel FCN based network called SDResU-Net for brain tumor segmentation, which simultaneously embeds dilated convolution and separable convolution into residual U-Net architecture. SDResU-Net introduces dilated block into a residual U-Net architecture, which largely expends the receptive field and gains better local and global feature descriptions capacity. Meanwhile, to fully utilize the channel and region information of MRI brain images, we separate the internal and inter-slice structures of the improved residual U-Net by employing separable convolution operator. The proposed SDResU-Net captures more pixel-level details and spatial information, which provides a considerable alternative for the automatic and accurate segmentation of brain tumors. Results and Conclusion: The proposed SDResU-Net is extensively evaluated on two public MRI brain image datasets, i.e., BraTS 2017 and BraTS 2018. Compared with its counterparts and stateof- the-arts, SDResU-Net gains superior performance on both datasets, showing its effectiveness. In addition, cross-validation results on two datasets illuminate its satisfying generalization ability.

For Chinaware fragments, it is difficult to assemble them directly without considering the wonderful patterns painted on them. Given the simplicity of the Chinaware designs, each object contains similar textures and patterns. Compared to the oddly diverse appliance modeling, the difference between fragments of different Chinaware is small. The fragments are small and the fracture is flat, and there are many potential matching objects. For the Chinaware fragments’ reassembly, most of the work is still done manually. There is little available fully automatic reassembly work, and these approaches are for the reassembly of a single Chinaware. But what reality demands is the reassembly of the multi-Chinaware. Therefore, this article proposes an automatic method, whose strategy is analogous to the manual assembly, to accomplish this complex task. First, segment the contours of fracture surfaces of the fragments by the geometric feature of fracture edge; then, the contours are matched using our proposed multi-scale linear feature descriptor; given the massive fragments of multiple objects, we use the inaccurate matching strategy to build a matching set for each fracture surface contour and perform contour matching in this set. In this article, the fracture surface is segmented using the 2D slope information on the fragment edge. The descriptor proposed in the article uses distance triangle towers and chained angles to describe the “undulations” of the fracture surface. Moreover, the article uses a strict absolute-advantage-principle to reject false matching. In addition, after the initial reconstruction, we will iteratively adjust fragments according to the gap between the fragments to achieve better multi-fragment matching results. In this article, 18 porcelains, a total of 103 pieces, have been tested. Experiments are also carried out for special cases, including fragment reassembly with missing fragments and fragment reassembly with redundant fragments. The experimental results showed the effectiveness of method. However, the types of experimental data we currently have are relatively simple, and there is no way to reassemble the fragments in minutes. We hope to enable faster reassembly of more fragments in the future.

Automatic sketch colorization is a challenging task that aims to generate a color image from a sketch, primarily due to its inherently ill-posed nature. While many approaches have shown promising results, two significant challenges remain: limited color patterns and a wide range of artifacts such as color bleeding and semantic inconsistencies among relevant regions. These issues stem from the operation of traditional convolutional structures, which capture structural features in a pixel-wise manner, resulting in inadequate utilization of regional information within the sketch. Therefore, we propose the Region-Assisted Sketch Coloring (RASC) method, which introduces an intermediate representation called the ’Region Map’ to explicitly characterize the regional information of the sketch. This Region Map is derived from the input sketch and is effectively formulated by our RASC architecture, enhancing the perception of region-wise features beyond the original pixel-wise features. Specifically, we start by employing the sketch encoder to extract hierarchical feature maps from the input sketches. Subsequently, we introduce a coarse-to-fine decoder comprising a series of Region-based Modulation (RM) blocks. This decoder modulates features that combine the modulation results of its previous block and the sketch features of the corresponding encoder block with our Region Formulation module. Each module explicitly formulates the sketch features in a region-wise manner. This accurately captures both the inner-region local style and inter-region global context dependency, resulting in various color patterns and fewer synthesis artifacts. Our experimental results show that our proposed method surpasses state-of-the-art methods in both synthetic and real sketch datasets.