Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets

no code implementations • 28 Mar 2024 • Tianyi Liu, Zhaorui Tan, Kaizhu Huang, Haochuan Jiang

Medical image segmentation presents the challenge of segmenting various-size targets, demanding the model to effectively capture both local and global information.

Image Segmentation Medical Image Segmentation +1

UGformer for Robust Left Atrium and Scar Segmentation Across Scanners

no code implementations • 11 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

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

no code implementations • 10 Jan 2022 • Lei LI, Fuping Wu, Sihan Wang, Xinzhe Luo, Carlos Martin-Isla, Shuwei Zhai, Jianpeng Zhang, Yanfei Liu7, Zhen Zhang, Markus J. Ankenbrand, Haochuan Jiang, Xiaoran Zhang, Linhong Wang, Tewodros Weldebirhan Arega, Elif Altunok, Zhou Zhao, Feiyan Li, Jun Ma, Xiaoping Yang, Elodie Puybareau, Ilkay Oksuz, Stephanie Bricq, Weisheng Li, Kumaradevan Punithakumar, Sotirios A. Tsaftaris, Laura M. Schreiber, Mingjing Yang, Guocai Liu, Yong Xia, Guotai Wang, Sergio Escalera, Xiahai Zhuang

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.

Data Augmentation Management +1

Semi-supervised Pathology Segmentation with Disentangled Representations

1 code implementation • 5 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 +1

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

1 code implementation • 5 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.

Management Segmentation

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

no code implementations • 11 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 implementation • 13 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.