Search Results for author: Heqin Zhu

Found 10 papers, 4 papers with code

Slide-SAM: Medical SAM Meets Sliding Window

1 code implementation16 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.

Anatomy Image Segmentation +3

UOD: Universal One-shot Detection of Anatomical Landmarks

1 code implementation13 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.

One-Shot Learning

Unsupervised augmentation optimization for few-shot medical image segmentation

no code implementations8 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.

Anatomy Few-Shot Semantic Segmentation +4

DATR: Domain-adaptive transformer for multi-domain landmark detection

no code implementations12 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.


DFTR: Depth-supervised Fusion Transformer for Salient Object Detection

no code implementations12 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.

Benchmarking Object +3

Relative distance matters for one-shot landmark detection

no code implementations3 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.

Contrastive Learning

You Only Learn Once: Universal Anatomical Landmark Detection

2 code implementations8 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.


Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models

1 code implementation16 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.

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