Search Results for author: Hongjie Yan

Found 3 papers, 1 papers with code

MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction

no code implementations20 Jun 2024 Luhui Cai, Weiming Zeng, Hongyu Chen, Hua Zhang, Yueyang Li, Hongjie Yan, Lingbin Bian, Nizhuan Wang

Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data.

Graph Learning Representation Learning

MSHCNet: Multi-Stream Hybridized Convolutional Networks with Mixed Statistics in Euclidean/Non-Euclidean Spaces and Its Application to Hyperspectral Image Classification

no code implementations7 Oct 2021 Shuang He, Haitong Tang, Xia Lu, Hongjie Yan, Nizhuan Wang

Specifically, our MSHCNet adopted four parallel streams, which contained G-stream, utilizing the irregular correlation between adjacent land covers in terms of first-order graph in non-Euclidean space; C-stream, adopting convolution operator to learn regular spatial-spectral features in Euclidean space; N-stream, combining first and second order features to learn representative and discriminative regular spatial-spectral features of Euclidean space; S-stream, using GSOP to capture boundary correlations and obtain graph representations from all nodes in graphs of non-Euclidean space.

Hyperspectral Image Classification

CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

1 code implementation1 Aug 2021 Haitong Tang, Shuang He, Mengduo Yang, Xia Lu, Qin Yu, Kaiyue Liu, Hongjie Yan, Nizhuan Wang

Through extensive analysis and experiments, we provided credible evidence showing that the multi-layer convolutional sparse coding block enables semantic segmentation model to converge faster, can extract finer semantic and appearance information of images, and improve the ability to recover spatial detail information.

Segmentation Semantic Segmentation

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