79 citations · 341 across the 40 of their papers we have counts for
10 papers · 1 filter
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
Hong Wang, Qi Xie, Yuexiang Li +3
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical trea…
Decoupled Mixup for Generalized Visual Recognition
Haozhe Liu, Wentian Zhang, Jinheng Xie +7
Convolutional neural networks (CNN) have demonstrated remarkable performance when the training and testing data are from the same distribution. However, such trained CNN models oft…
A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays
Haoqin Ji, Haozhe Liu, Yuexiang Li +7
Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed…
Combating Mode Collapse in GANs via Manifold Entropy Estimation
Haozhe Liu, Bing Li, Haoqian Wu +5
Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications in recent years. However, mode collapse remains a critical problem in GANs. I…
Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction
Hong Wang, Yuexiang Li, Deyu Meng +1
Inspired by the great success of deep neural networks, learning-based methods have gained promising performances for metal artifact reduction (MAR) in computed tomography (CT) imag…
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation
Yao Zhang, Nanjun He, Jiawei Yang +6
Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possibl…
Robust Representation via Dynamic Feature Aggregation
Haozhe Liu, Haoqin Ji, Yuexiang Li +5
Deep convolutional neural network (CNN) based models are vulnerable to the adversarial attacks. One of the possible reasons is that the embedding space of CNN based model is sparse…
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
Heqin Zhu, Xu Sun, Yuexiang Li +3
Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than…
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images
Hong Liu, Dong Wei, Donghuan Lu +4
Automated surface segmentation of retinal layer is important and challenging in analyzing optical coherence tomography (OCT). Recently, many deep learning based methods have been d…
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training
Yi Lin, Zhiyong Qu, Hao Chen +6
Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on t…