no code implementations • 12 Feb 2025 • Tianxiang Zhang, Zhaokun Wen, Bo Kong, Kecheng Liu, Yisi Zhang, Peixian Zhuang, Jiangyun Li
Referring Remote Sensing Image Segmentation (RRSIS) is critical for ecological monitoring, urban planning, and disaster management, requiring precise segmentation of objects in remote sensing imagery guided by textual descriptions.
no code implementations • 28 Oct 2023 • Haoran Shen, Yifu Zhang, Wenxuan Wang, Chen Chen, Jing Liu, Shanshan Song, Jiangyun Li
As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i. e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices.
no code implementations • 19 May 2023 • Wenxuan Wang, Jing Liu, Xingjian He, Yisi Zhang, Chen Chen, Jiachen Shen, Yan Zhang, Jiangyun Li
Referring image segmentation (RIS) is a fundamental vision-language task that intends to segment a desired object from an image based on a given natural language expression.
1 code implementation • 21 Apr 2023 • Wenxuan Wang, Jing Wang, Chen Chen, Jianbo Jiao, Yuanxiu Cai, Shanshan Song, Jiangyun Li
The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data.
no code implementations • 21 Apr 2023 • Jiachen Shen, Wenxuan Wang, Chen Chen, Jianbo Jiao, Jing Liu, Yan Zhang, Shanshan Song, Jiangyun Li
Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner.
no code implementations • 11 Nov 2022 • Shanshan Song, Jiangyun Li, Jing Wang, Yuanxiu Cai, Wenkai Dong
There is a key problem in the medical visual question answering task that how to effectively realize the feature fusion of language and medical images with limited datasets.
no code implementations • 4 Jul 2022 • Jing Wang, Jiangyun Li, Wei Li, Lingfei Xuan, Tianxiang Zhang, Wenxuan Wang
The contextual information is critical for various computer vision tasks, previous works commonly design plug-and-play modules and structural losses to effectively extract and aggregate the global context.
no code implementations • 14 Jun 2022 • Wenxuan Wang, Chen Chen, Jing Wang, Sen Zha, Yan Zhang, Jiangyun Li
For 3D medical image (e. g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly.
1 code implementation • 9 Apr 2022 • Jiangyun Li, Sen Zha, Chen Chen, Meng Ding, Tianxiang Zhang, Hong Yu
First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts.
1 code implementation • 29 Mar 2022 • Jiangyun Li, Hong Yu, Chen Chen, Meng Ding, Sen Zha
In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost.
1 code implementation • 30 Jan 2022 • Jiangyun Li, Wenxuan Wang, Chen Chen, Tianxiang Zhang, Sen Zha, Jing Wang, Hong Yu
Different from TransBTS, the proposed TransBTSV2 is not limited to brain tumor segmentation (BTS) but focuses on general medical image segmentation, providing a stronger and more efficient 3D baseline for volumetric segmentation of medical images.
2 code implementations • 7 Mar 2021 • Wenxuan Wang, Chen Chen, Meng Ding, Jiangyun Li, Hong Yu, Sen Zha
To capture the local 3D context information, the encoder first utilizes 3D CNN to extract the volumetric spatial feature maps.
Ranked #1 on
Brain Tumor Segmentation
on BRATS 2019
(Dice Score metric)
1 code implementation • 6 Apr 2019 • Chen Chen, Xiaopeng Liu, Meng Ding, Junfeng Zheng, Jiangyun Li
In this work, we aim to segment brain MRI volumes.