44 citations · 130 across the 28 of their papers we have counts for
19 papers · 1 filter
Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation
Hong Liu, Dong Wei, Qian Dai +3
Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications.…
Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation
Hong Liu, Dong Wei, Qiong Peng +4
Computed Tomography Report Generation (CTRG) aims to automate the clinical radiology reporting process, thereby reducing the workload of report writing and facilitating patient car…
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation
Zhipeng Deng, Zhe Xu, Tsuyoshi Isshiki +1
Medical image segmentation is challenging due to the diversity of medical images and the lack of labeled data, which motivates recent developments in federated semi-supervised lear…
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation
Qian Dai, Dong Wei, Hong Liu +3
Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications.…
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray
Jinghan Sun, Dong Wei, Zhe Xu +4
Chest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workf…
RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training
Lianyu Zhou, Dong Wei, Donghuan Lu +3
As an essential indicator for cancer progression and treatment response, tumor size is often measured following the response evaluation criteria in solid tumors (RECIST) guideline…
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Munan Ning, Donghuan Lu, Yujia Xie +6
Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data. Unfortunately, mapping the target-domain distribution to the source-domain unconditionally…
Lesion Guided Explainable Few Weak-shot Medical Report Generation
Jinghan Sun, Dong Wei, Liansheng Wang +1
Medical images are widely used in clinical practice for diagnosis. Automatically generating interpretable medical reports can reduce radiologists' burden and facilitate timely care…
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
Xinyu Shi, Dong Wei, Yu Zhang +5
Research into Few-shot Semantic Segmentation (FSS) has attracted great attention, with the goal to segment target objects in a query image given only a few annotated support images…
Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation
Cong Xie, Hualuo Liu, Shilei Cao +4
Semantic segmentation is important in medical image analysis. Inspired by the strong ability of traditional image analysis techniques in capturing shape priors and inter-subject si…
Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation
Cheng Bian, Chenglang Yuan, Kai Ma +3
Due to the lack of properly annotated medical data, exploring the generalization capability of the deep model is becoming a public concern. Zero-shot learning (ZSL) has emerged in…
Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification
Jinghan Sun, Dong Wei, Kai Ma +2
Rare diseases are characterized by low prevalence and are often chronically debilitating or life-threatening. Imaging-based classification of rare diseases is challenging due to th…
Multi-Anchor Active Domain Adaptation for Semantic Segmentation
Munan Ning, Donghuan Lu, Dong Wei +5
Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the…
A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework
Munan Ning, Cheng Bian, Dong Wei +5
Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to anothe…
RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation
Cong Xie, Shilei Cao, Dong Wei +6
Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance. Consider…
Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration
Zhe Xu, Jie Luo, Donghuan Lu +7
In order to tackle the difficulty associated with the ill-posed nature of the image registration problem, regularization is often used to constrain the solution space. For most lea…
Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation
Hong-Yu Zhou, Hualuo Liu, Shilei Cao +5
Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given sever…
Superpixel-Guided Label Softening for Medical Image Segmentation
Hang Li, Dong Wei, Shilei Cao +3
Segmentation of objects of interest is one of the central tasks in medical image analysis, which is indispensable for quantitative analysis. When developing machine-learning based…
LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation
Shuxin Wang, Shilei Cao, Dong Wei +5
We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images. The main idea is to treat one-shot segmentation as a classical atlas-ba…