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Found 19 papers, 0 papers with code
The Annealing Sparse Bayesian Learning Algorithm
The performance such as NMSE and F-measure can be greatly improved due to the annealing technique.
Fast Marginalized Block Sparse Bayesian Learning Algorithm
One typical correlation structure is the intra-block correlation in block sparse signals.
Scan-based Compressed Terahertz Imaging and Real-Time Reconstruction via the Complex-valued Fast Block Sparse Bayesian Learning Algorithm
Compressed Sensing based Terahertz imaging (CS-THz) is a computational imaging technique.
Multi-Stream Single Shot Spatial-Temporal Action Detection
We present a 3D Convolutional Neural Networks (CNNs) based single shot detector for spatial-temporal action detection tasks.
A Deep Reinforcement Learning Approach to Multi-component Job Scheduling in Edge Computing
We are interested in the optimal scheduling of a collection of multi-component application jobs in an edge computing system that consists of geo-distributed edge computing nodes connected through a wide area network.
3D Anchor-Free Lesion Detector on Computed Tomography Scans
The anchor mechanism is removed and lesions are formalized as single keypoints.
AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy
Though challenging, with the great advances in object detection techniques, automated polyp detection still demonstrates a great potential in reducing the false negative rate while maintaining a high precision.
Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features
We analyze the lesion-vs-image scale carefully and propose a large-size feature pyramid network (LFPN) to preserve more image details for mini lesion instance detection.
Colorectal Polyp Segmentation by U-Net with Dilation Convolution
The model we design consists of an encoder to extract multi-scale semantic features and a decoder to expand the feature maps to a polyp segmentation map.
3D Aggregated Faster R-CNN for General Lesion Detection
This classifier branch is equipped with Feature Aggregation and Local Magnification Layers to enhance the classifier branch.
Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images
The first one is that our dataset is not fully labeled, i. e., only a subset of all lesion instances are marked.
Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks
This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) to construct an effective classifier of ROP stages 1-3 based on neonatal retinal images.
Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study
All these factors pose a significant challenge to effective polyp detection in a colonoscopy.
Financial Markets Prediction with Deep Learning
Financial markets are difficult to predict due to its complex systems dynamics.
CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets
Financial markets are a complex dynamical system.
DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar
Built on our recent proposed 3DRIMR (3D Reconstruction and Imaging via mmWave Radar), we introduce in this paper DeepPoint, a deep learning model that generates 3D objects in point cloud format that significantly outperforms the original 3DRIMR design.
A Knowledge-Based Decision Support System for In Vitro Fertilization Treatment
To improve the IVF success rate, we propose a knowledge-based decision support system that can provide medical advice on the treatment protocol and medication adjustment for each patient visit during IVF treatment cycle.
Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements
It is a challenging task to predict financial markets.
R2P: A Deep Learning Model from mmWave Radar to Point Cloud
Recent research has shown the effectiveness of mmWave radar sensing for object detection in low visibility environments, which makes it an ideal technique in autonomous navigation systems.
