Search Results for author: Haoyue Zhang

Found 6 papers, 3 papers with code

MovePose: A High-performance Human Pose Estimation Algorithm on Mobile and Edge Devices

no code implementations17 Aug 2023 Dongyang Yu, Haoyue Zhang, Ruisheng Zhao, Guoqi Chen, Wangpeng An, Yanhong Yang

It aims to maintain real-time performance while improving the accuracy of human posture estimation for mobile devices.

Pose Estimation

Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models

no code implementations8 Feb 2023 Haoyue Zhang, Jennifer S. Polson, Eric J. Yang, Kambiz Nael, William Speier, Corey W. Arnold

This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.

Computed Tomography (CT) Decision Making

MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients

1 code implementation13 Jun 2022 Wen Tang, Haoyue Zhang, Pengxin Yu, Han Kang, Rongguo Zhang

Several deep learning-based methods are proposed for the OS time prediction on multi-modal MRI problems.

RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans

1 code implementation13 Jun 2022 Pengxin Yu, Haoyue Zhang, Han Kang, Wen Tang, Corey W. Arnold, Rongguo Zhang

In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost.

Medical Diagnosis SSIM +1

Transformer Lesion Tracker

1 code implementation13 Jun 2022 Wen Tang, Han Kang, Haoyue Zhang, Pengxin Yu, Corey W. Arnold, Rongguo Zhang

Previous methods typically lack the integration of local and global information.

Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time

no code implementations5 Nov 2020 Haoyue Zhang, Jennifer S Polson, Kambiz Nael, Noriko Salamon, Bryan Yoo, Suzie El-Saden, Fabien Scalzo, William Speier, Corey W Arnold

We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0. 74, with a sensitivity of 0. 70 and a specificity of 0. 81 for classifying TSS < 4. 5 hours.

Specificity Transfer Learning

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