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Found 9 papers, 4 papers with code
A multi-stage semi-supervised learning for ankle fracture classification on CT images
Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic.
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images
Complex scenario of ultrasound image, in which adjacent tissues (i. e., background) share similar intensity with and even contain richer texture patterns than lesion region (i. e., foreground), brings a unique challenge for accurate lesion segmentation.
Multi-View Imputation and Cross-Attention Network Based on Incomplete Longitudinal and Multimodal Data for Conversion Prediction of Mild Cognitive Impairment
To address these challenges, a multi-view imputation and cross-attention network (MCNet) was proposed to integrate data imputation and MCI conversion prediction in a unified framework.
Semi-Supervised Hybrid Spine Network for Segmentation of Spine MR Images
In the first stage, we constructed a 2D semi-supervised DeepLabv3+ by using cross pseudo supervision to obtain intra-slice features and coarse segmentation.
SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation
The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement.
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution.
CF2-Net: Coarse-to-Fine Fusion Convolutional Network for Breast Ultrasound Image Segmentation
Breast ultrasound (BUS) image segmentation plays a crucial role in a computer-aided diagnosis system, which is regarded as a useful tool to help increase the accuracy of breast cancer diagnosis.
Cross-lingual Data Transformation and Combination for Text Classification
In order to train a generalizable model, a large volume of text must be collected.
Direct Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network
The CARN architecture is composed of a cascade amplifier network (CAN) for expressive feature embedding and a linear regression model for multiple indices estimation.
