59 citations · 92 across the 18 of their papers we have counts for
12 papers · 1 filter
APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation
Zikang Xu, Fenghe Tang, Quan Quan +2
Ensuring fairness in deep-learning-based segmentors is crucial for health equity. Much effort has been dedicated to mitigating unfairness in the training datasets or procedures. Ho…
UOD: Universal One-shot Detection of Anatomical Landmarks
Heqin Zhu, Quan Quan, Qingsong Yao +2
One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly…
Unsupervised augmentation optimization for few-shot medical image segmentation
Quan Quan, Shang Zhao, Qingsong Yao +2
The augmentation parameters matter to few-shot semantic segmentation since they directly affect the training outcome by feeding the networks with varying perturbated samples. Howev…
Information-guided pixel augmentation for pixel-wise contrastive learning
Quan Quan, Qingsong Yao, Jun Li +1
Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks. Different from widely studied instance-level contrastive learning, pixel…
Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review
Zikang Xu, Jun Li, Qingsong Yao +3
Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity i…
DATR: Domain-adaptive transformer for multi-domain landmark detection
Heqin Zhu, Qingsong Yao, S. Kevin Zhou
Accurate anatomical landmark detection plays an increasingly vital role in medical image analysis. Although existing methods achieve satisfying performance, they are mostly based o…
Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection
Qingsong Yao, Zhen Huang, Ao Wang +4
Medical landmark detection usually requires accurate expert annotations, which are laborious and difficult to scale across anatomical regions. In this work, we study an extreme ann…
Medical Aegis: Robust adversarial protectors for medical images
Qingsong Yao, Zecheng He, S. Kevin Zhou
Deep neural network based medical image systems are vulnerable to adversarial examples. Many defense mechanisms have been proposed in the literature, however, the existing defenses…
One-Shot Medical Landmark Detection
Qingsong Yao, Quan Quan, Li Xiao +1
The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medica…
You Only Learn Once: Universal Anatomical Landmark Detection
Heqin Zhu, Qingsong Yao, Li Xiao +1
Detecting anatomical landmarks in medical images plays an essential role in understanding the anatomy and planning automated processing. In recent years, a variety of deep neural n…
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks
Qingsong Yao, Zecheng He, Yi Lin +3
Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making. Luckily, medical A…
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection
Qingsong Yao, Zecheng He, Hu Han +1
Recent methods in multiple landmark detection based on deep convolutional neural networks (CNNs) reach high accuracy and improve traditional clinical workflow. However, the vulnera…