133 citations · 346 across the 21 of their papers we have counts for
11 papers · 1 filter
C^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model
Wei Li, Jingyang Zhang, Guoan Wang +4
Universal segmentation models exhibit significant potential for diverse tasks involving different imaging modalities and segmentation objectives. Task-Incremental Learning provides…
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Jingyang Zhang, Nathan Inkawhich, Randolph Linderman +3
Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which…
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation
Ran Gu, Guotai Wang, Jiangshan Lu +8
Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to d…
Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures
Ran Gu, Jingyang Zhang, Guotai Wang +5
Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for medical image segmentation, yet need plenty of manual annotations for training. Semi-Supervised…
Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation
Ran Gu, Jiangshan Lu, Jingyang Zhang +4
Efficiently utilizing discriminative features is crucial for convolutional neural networks to achieve remarkable performance in medical image segmentation and is also important for…
MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning
Xiangde Luo, Guotai Wang, Tao Song +6
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Ne…
Visibility-aware Multi-view Stereo Network
Jingyang Zhang, Yao Yao, Shiwei Li +2
Learning-based multi-view stereo (MVS) methods have demonstrated promising results. However, very few existing networks explicitly take the pixel-wise visibility into consideration…
Learning Stereo Matchability in Disparity Regression Networks
Jingyang Zhang, Yao Yao, Zixin Luo +4
Learning-based stereo matching has recently achieved promising results, yet still suffers difficulties in establishing reliable matches in weakly matchable regions that are texture…
Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation
Jingyang Zhang, Guotai Wang, Hongzhi Xie +4
The segmentation of coronary arteries in X-ray angiograms by convolutional neural networks (CNNs) is promising yet limited by the requirement of precisely annotating all pixels in…
BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
Yao Yao, Zixin Luo, Shiwei Li +5
While deep learning has recently achieved great success on multi-view stereo (MVS), limited training data makes the trained model hard to be generalized to unseen scenarios. Compar…
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack
Jingyang Zhang, Hsin-Pai Cheng, Chunpeng Wu +2
In recent years, deep neural networks demonstrated state-of-the-art performance in a large variety of tasks and therefore have been adopted in many applications. On the other hand,…