Search Results for author:
Found 7 papers, 3 papers with code
3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations
Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images; secondly, based on the similarity of the tree-shaped morphology between 2D and 3D vessels, the Crop-and-Overlap strategy is presented to generate reference masks from 2D structure-agnostic vessel annotations, which are fit for varied vascular structures, and the adversarial loss is introduced to guide the tree-shaped morphology of 3D vessels; thirdly, the temporal consistency loss is proposed to foster the training stability and keep the model updated smoothly.
YOLO-FaceV2: A Scale and Occlusion Aware Face Detector
In this paper, we propose a real-time face detector based on the one-stage detector YOLOv5, named YOLO-FaceV2.
Unsupervised Local Discrimination for Medical Images
To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images.
COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19
In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19.
Depthwise Multiception Convolution for Reducing Network Parameters without Sacrificing Accuracy
We have carried out the experiment on four benchmark datasets, i. e. Cifar-10, Cifar-100, STL-10 and ImageNet32x32, using five popular CNN models, Multiception achieved accuracy promotion in all models and demonstrated higher accuracy performance compared to related works.
Building Large Chinese Corpus for Spoken Dialogue Research in Specific Domains
To our best knowledge, this is the largest Chinese spoken dialogue corpus, as well as the first one with slot information.
Visual Analytics of Movement Pattern Based on Time-Spatial Data: A Neural Net Approach
This method aggregates three main parts that are Back-end Data Model, Neural Net Algorithm including clustering method Self-Organizing Map (SOM) and prediction approach Recurrent Neural Net (RNN) for ex- tracting the features and lastly a solid front-end that displays the results to users with an interactive system.
