Jiayue Cai

Orcid: 0000-0003-2984-500X

According to our database1, Jiayue Cai authored at least 11 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
AI-Enhanced 7-Point Checklist for Melanoma Detection Using Clinical Knowledge Graphs and Data-Driven Quantification.
CoRR, 2024

2023
SSD-KD: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images.
Medical Image Anal., 2023

Brainstem Functional Parcellation Based on Spatial Connectivity Features Using Functional Magnetic Resonance Imaging.
Proceedings of the Artificial Intelligence - Third CAAI International Conference, 2023

2022
Multi-channel content based image retrieval method for skin diseases using similarity network fusion and deep community analysis.
Biomed. Signal Process. Control., 2022

2021
Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection.
Comput. Biol. Medicine, 2021

2020
Novel Regional Activity Representation With Constrained Canonical Correlation Analysis for Brain Connectivity Network Estimation.
IEEE Trans. Medical Imaging, 2020

Parkinson's Disease Detection from fMRI-Derived Brainstem Regional Functional Connectivity Networks.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

2019
Dynamic Graph Theoretical Analysis of Functional Connectivity in Parkinson's Disease: The Importance of Fiedler Value.
IEEE J. Biomed. Health Informatics, 2019

Classifying Melanoma and Seborrheic Keratosis Automatically with Polarization Speckle Imaging.
Proceedings of the 2019 IEEE Global Conference on Signal and Information Processing, 2019

2017
Assessing functional connectivity of brainstem nuclei in fMRI data.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

2016
Feature selection to simplify BDI for efficient depression identification.
Proceedings of the 2016 IEEE Canadian Conference on Electrical and Computer Engineering, 2016


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