PL-Net: progressive learning network for medical image segmentation.

In recent years, deep convolutional neural network-based segmentation methods have achieved state-of-the-art performance for many medical analysis tasks. However, most of these approaches rely on optimizing the U-Net structure or adding new functional modules, which overlooks the complementation and fusion of coarse-grained and fine-grained semantic information. To address these issues, we propose a 2D medical image segmentation framework called Progressive Learning Network (PL-Net), which comprises Internal Progressive Learning (IPL) and External Progressive Learning (EPL).

Artificial intelligence-assisted diagnosis of congenital heart disease and associated pulmonary arterial hypertension from chest radiographs: A multi-reader multi-case study.

Objectives: To explore the possibility of automatic diagnosis of congenital heart disease (CHD) and pulmonary arterial hypertension associated with CHD (PAH-CHD) from chest radiographs using artificial intelligence (AI) technology and to evaluate whether AI assistance could improve clinical diagnostic accuracy.

Materials And Methods: A total of 3255 frontal preoperative chest radiographs (1174 CHD of any type and 2081 non-CHD) were retrospectively obtained. In this study, we adopted ResNet18 pretrained with the ImageNet database to establish diagnostic models.

SCA-Former: transformer-like network based on stream-cross attention for medical image segmentation.

. Deep convolutional neural networks (CNNs) have been widely applied in medical image analysis and achieved satisfactory performances. While most CNN-based methods exhibit strong feature representation capabilities, they face challenges in encoding long-range interaction information due to the limited receptive fields.

Mapping the knowledge domain of financial decision making: A scientometric and bibliometric study.

Based on a 12-year bibliographic record collected from the Web of Science (Thomson Reuters) database, the present study aims to provide a macroscopic overview of the knowledge domain in financial decision making (FDM). A scientometric and bibliometric analysis was conducted on the literature published in the field from 2010 to 2021, using the CiteSpace software. The analysis focuses on the co-occurring categories, the geographic distributions, the vital references, the distribution of topics, as well as the research fronts and emerging trends of financial related decision making.

FSOU-Net: Feature supplement and optimization U-Net for 2D medical image segmentation.

Background: The results of medical image segmentation can provide reliable evidence for clinical diagnosis and treatment. The U-Net proposed previously has been widely used in the field of medical image segmentation. Its encoder extracts semantic features of different scales at different stages, but does not carry out special processing for semantic features of each scale.

ResGANet: Residual group attention network for medical image classification and segmentation.

In recent years, deep learning technology has shown superior performance in different fields of medical image analysis. Some deep learning architectures have been proposed and used for computational pathology classification, segmentation, and detection tasks. Due to their simple, modular structure, most downstream applications still use ResNet and its variants as the backbone network.

DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation.

Background And Objective: Segmentation is a key step in biomedical image analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly applied in the field of medical image processing; however, standard models still have some drawbacks. Due to the significant loss of spatial information at the coding stage, it is often difficult to restore the details of low-level visual features using simple deconvolution, and the generated feature maps are sparse, which results in performance degradation.

Medical image segmentation using boundary-enhanced guided packet rotation dual attention decoder network.

Background: The automatic segmentation of medical images is an important task in clinical applications. However, due to the complexity of the background of the organs, the unclear boundary, and the variable size of different organs, some of the features are lost during network learning, and the segmentation accuracy is low.

Objective: To address these issues, this prompted us to study whether it is possible to better preserve the deep feature information of the image and solve the problem of low segmentation caused by unclear image boundaries.

Fully convolutional attention network for biomedical image segmentation.

In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks efficiently and accurately. Different from previous work on image segmentation through multiscale feature fusion, we propose the fully convolutional attention network (FCANet) to aggregate contextual information at long-range and short-range distances. Specifically, we add two types of attention modules, the spatial attention module and the channel attention module, to the Res2Net network, which has a dilated strategy.