Search Results for author:
Found 11 papers, 3 papers with code
Make it more specific: A novel uncertainty based airway segmentation application on 3D U-Net and its variants
The most popular algorithms in medical segmentation, 3D U-Net and its variants, can directly implement the task of lung trachea segmentation, but its failure to consider the special tree-like structure of the trachea suggests that there is much room for improvement in its segmentation accuracy.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation
We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D Computed Tomography (CT) segmentation.
Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19
Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools.
A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans
Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation.
Optimal transport for model calibration
We provide a survey of recent results on model calibration by Optimal Transport.
Detailed 2D-3D Joint Representation for Human-Object Interaction
In light of these, we propose a detailed 2D-3D joint representation learning method.
Ranked #1 on
Human-Object Interaction Detection
on Ambiguious-HOI
Joint Modelling and Calibration of SPX and VIX by Optimal Transport
This paper addresses the joint calibration problem of SPX options and VIX options or futures.
PaStaNet: Toward Human Activity Knowledge Engine
In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics.
Ranked #3 on
Human-Object Interaction Detection
on HICO
Calibration of Local-Stochastic Volatility Models by Optimal Transport
In this paper, we study a semi-martingale optimal transport problem and its application to the calibration of Local-Stochastic Volatility (LSV) models.
HAKE: Human Activity Knowledge Engine
To address these and promote the activity understanding, we build a large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states.
Ranked #2 on
Human-Object Interaction Detection
on HICO
(using extra training data)
