15 citations · 51 across the 10 of their papers we have counts for
10 papers
Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation
Shengbo Gao, Ziji Zhang, Jiechao Ma +2
Semi-supervised learning has become increasingly popular in medical image segmentation due to its ability to leverage large amounts of unlabeled data to extract additional informat…
A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics
Hong-Yu Zhou, Yizhou Yu, Chengdi Wang +7
During the diagnostic process, clinicians leverage multimodal information, such as chief complaints, medical images, and laboratory-test results. Deep-learning models for aiding di…
ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding
Le Xue, Ning Yu, Shu Zhang +8
Recent advancements in multimodal pre-training have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts,…
Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework
Shu Zhang, Ran Xu, Caiming Xiong +1
Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In…
Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training
Shu Zhang, Zihao Li, Hong-Yu Zhou +2
The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications. As a result, constructing…
SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation
Hong-Yu Zhou, Chengdi Wang, Haofeng Li +4
Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised met…
A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
Jie Lian, Jingyu Liu, Shu Zhang +4
Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and dise…
Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices
Shu Zhang, Jincheng Xu, Yu-Chun Chen +4
Universal lesion detection from computed tomography (CT) slices is important for comprehensive disease screening. Since each lesion can locate in multiple adjacent slices, 3D conte…
Deep Homography Estimation for Dynamic Scenes
Hoang Le, Feng Liu, Shu Zhang +1
Homography estimation is an important step in many computer vision problems. Recently, deep neural network methods have shown to be favorable for this problem when compared to trad…
MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection
Zihao Li, Shu Zhang, Junge Zhang +3
Universal lesion detection (ULD) on computed tomography (CT) images is an important but underdeveloped problem. Recently, deep learning-based approaches have been proposed for ULD,…