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Found 10 papers, 5 papers with code
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts
Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs.
ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection
Accurate polyp detection is critical for early colorectal cancer diagnosis.
SCPMan: Shape Context and Prior Constrained Multi-scale Attention Network for Pancreatic Segmentation
Specifically, we proposed a Multi-scale Feature Extraction Module (MFE) and a Mixed-scale Attention Integration Module (MAI) to address unclear pancreas boundaries.
GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance
Our proposed GSmoothFace model mainly consists of the Audio to Expression Prediction (A2EP) module and the Target Adaptive Face Translation (TAFT) module.
Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey
In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis.
LKD-Net: Large Kernel Convolution Network for Single Image Dehazing
The designed DLKCB can split the deep-wise large kernel convolution into a smaller depth-wise convolution and a depth-wise dilated convolution without introducing massive parameters and computational overhead.
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow
In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows.
PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery
In this paper, we address a fundamental problem in PCSR: How to downsample the dense point cloud with arbitrary scales while preserving the local topology of discarding points in a case-agnostic manner (i. e. without additional storage for point relationship)?
A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations
To address the issue, we propose a novel two-level routing component to the queueing network model.
Self-supervised asymmetric deep hashing with margin-scalable constraint
In this paper, we propose a novel self-supervised asymmetric deep hashing method with a margin-scalable constraint(SADH) approach to cope with these problems.
