Search Results for author: Haochuan Jiang

Found 6 papers, 3 papers with code

UGformer for Robust Left Atrium and Scar Segmentation Across Scanners

no code implementations11 Oct 2022 Tianyi Liu, Size Hou, Jiayuan Zhu, Zilong Zhao, Haochuan Jiang

an enhanced transformer module with deformable convolutions to improve the blending of the transformer information with convolutional information and help predict irregular LAs and scar shapes.

Domain Generalization Image Segmentation +2

Semi-supervised Pathology Segmentation with Disentangled Representations

1 code implementation5 Sep 2020 Haochuan Jiang, Agisilaos Chartsias, Xinheng Zhang, Giorgos Papanastasiou, Scott Semple, Mark Dweck, David Semple, Rohan Dharmakumar, Sotirios A. Tsaftaris

The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations.

Anatomy Disentanglement

Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling

1 code implementation5 Sep 2020 Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias, Sotirios A. Tsaftaris

Together with the corresponding encoding features, these representations are propagated to decoding layers with U-Net skip-connections.


Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

no code implementations11 Jun 2019 Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, Yilin Xiong

To capture the sequential dependencies, existing methods resort either to data augmentation techniques or left-to-right style autoregressive training. Since these methods are aimed to model the sequential nature of user behaviors, they ignore the future data of a target interaction when constructing the prediction model for it.

Data Augmentation Sequential Recommendation +1

W-Net : One-Shot Arbitrary-StyleChinese Character Generationwith Deep Neural Networks

1 code implementation13 Dec 2018 Haochuan Jiang, Guanyu Yang, Kaizhu Huang, and Rui ZHANG

Due to the huge category number, the sophisticated com-binations of various strokes and radicals, and the free writing or print-ing styles, generating Chinese characters with diverse styles is alwaysconsidered as a difficult task.

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