1 code implementation • 16 Nov 2023 • Quan Quan, Fenghe Tang, Zikang Xu, Heqin Zhu, S. Kevin Zhou
To address these problems, we propose Slide-SAM, which treats a stack of three adjacent slices as a prediction window.
1 code implementation • 13 Jun 2023 • Heqin Zhu, Quan Quan, Qingsong Yao, Zaiyi Liu, S. Kevin Zhou
However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data.
no code implementations • 8 Jun 2023 • Quan Quan, Shang Zhao, Qingsong Yao, Heqin Zhu, S. Kevin Zhou
The augmentation parameters matter to few-shot semantic segmentation since they directly affect the training outcome by feeding the networks with varying perturbated samples.
no code implementations • 7 May 2023 • Zhen Huang, Han Li, Shitong Shao, Heqin Zhu, Huijie Hu, Zhiwei Cheng, Jianji Wang, S. Kevin Zhou
The pelvis, the lower part of the trunk, supports and balances the trunk.
no code implementations • 12 Mar 2022 • Heqin Zhu, Qingsong Yao, S. Kevin Zhou
In this work, we propose a universal model for multi-domain landmark detection by taking advantage of transformer for modeling long dependencies and develop a domain-adaptive transformer model, named as DATR, which is trained on multiple mixed datasets from different anatomies and capable of detecting landmarks of any image from those anatomies.
no code implementations • 12 Mar 2022 • Heqin Zhu, Xu sun, Yuexiang Li, Kai Ma, S. Kevin Zhou, Yefeng Zheng
This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture.
no code implementations • 3 Mar 2022 • Qingsong Yao, Jianji Wang, Yihua Sun, Quan Quan, Heqin Zhu, S. Kevin Zhou
Contrastive learning based methods such as cascade comparing to detect (CC2D) have shown great potential for one-shot medical landmark detection.
2 code implementations • 8 Mar 2021 • Heqin Zhu, Qingsong Yao, Li Xiao, S. Kevin Zhou
However, all of those methods are unary in the sense that a highly specialized network is trained for a single task say associated with a particular anatomical region.
1 code implementation • 16 Dec 2020 • Pengbo Liu, Hu Han, Yuanqi Du, Heqin Zhu, Yinhao Li, Feng Gu, Honghu Xiao, Jun Li, Chunpeng Zhao, Li Xiao, Xinbao Wu, S. Kevin Zhou
Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.
no code implementations • 2 Jan 2020 • Yuanyuan Lyu, Haofu Liao, Heqin Zhu, S. Kevin Zhou
In contrast, there exists a wealth of artifact-free, high quality CT images with vertebra annotations.