4k citations · 4.5k across the 31 of their papers we have counts for
31 papers
A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation
Jianghao Wu, Xiangde Luo, Yubo Zhou +3
Test-Time Adaptation (TTA) offers a practical solution for deploying image segmentation models under domain shift without accessing source data or retraining. Among existing TTA st…
MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation
Yiwen Ye, Yicheng Wu, Xiangde Luo +5
Foundation models have become a promising paradigm for advancing medical image analysis, particularly for segmentation tasks where downstream applications often emerge sequentially…
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
Han Zhang, Xiangde Luo, Yong Chen +1
Annotation variability remains a substantial challenge in medical image segmentation, stemming from ambiguous imaging boundaries and diverse clinical expertise. Traditional deep le…
Dynamic Gradient Sparsification Training for Few-Shot Fine-tuning of CT Lymph Node Segmentation Foundation Model
Zihao Luo, Zijun Gao, Wenjun Liao +3
Accurate lymph node (LN) segmentation is critical in radiotherapy treatment and prognosis analysis, but is limited by the need for large annotated datasets. While deep learning-bas…
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction
Tao Song, Yicheng Wu, Minhao Hu +6
Accelerated MRI reconstruction plays a vital role in reducing scan time while preserving image quality. While most existing methods rely on complex-valued image-space or k-space da…
Learning Modality-Aware Representations: Adaptive Group-wise Interaction Network for Multimodal MRI Synthesis
Tao Song, Yicheng Wu, Minhao Hu +6
Multimodal MR image synthesis aims to generate missing modality images by effectively fusing and mapping from a subset of available MRI modalities. Most existing methods adopt an i…
Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning
Litingyu Wang, Yijie Qu, Xiangde Luo +3
Assessing the presence of potentially malignant lymph nodes aids in estimating cancer progression, and identifying surrounding benign lymph nodes can assist in determining potentia…
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation
Zihao Luo, Xiangde Luo, Zijun Gao +1
Deep learning models have exhibited remarkable efficacy in accurately delineating the prostate for diagnosis and treatment of prostate diseases, but challenges persist in achieving…
Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases
Xiangde Luo, Zihan Li, Shaoting Zhang +2
Deep learning has enabled great strides in abdominal multi-organ segmentation, even surpassing junior oncologists on common cases or organs. However, robustness on corner cases and…
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
Hongqiu Wang, Xiangde Luo, Wu Chen +4
Accurate vessel segmentation in Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images is crucial for diagnosing retinal diseases. Although recent techniques have shown en…
Diversified and Personalized Multi-rater Medical Image Segmentation
Yicheng Wu, Xiangde Luo, Zhe Xu +5
Annotation ambiguity due to inherent data uncertainties such as blurred boundaries in medical scans and different observer expertise and preferences has become a major obstacle for…
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Xiangde Luo, Jia Fu, Yunxin Zhong +39
Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is c…
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals
Hongqiu Wang, Jian Chen, Shichen Zhang +6
Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. Precise delineation of the Gross Tumor Volume…
Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency
Meng Han, Xiangde Luo, Wenjun Liao +3
Multi-organ segmentation in abdominal Computed Tomography (CT) images is of great importance for diagnosis of abdominal lesions and subsequent treatment planning. Though deep learn…
ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding
Zihan Li, Yuan Zheng, Xiangde Luo +2
Medical image segmentation plays a critical role in clinical decision-making, treatment planning, and disease monitoring. However, accurate segmentation of medical images is challe…
MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset
Guotai Wang, Jianghao Wu, Xiangde Luo +3
Pretraining with large-scale 3D volumes has a potential for improving the segmentation performance on a target medical image dataset where the training images and annotations are l…
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
Guotai Wang, Xiangde Luo, Ran Gu +6
Background and Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models. Existing toolkits mainly focus on fully…
PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation
Shuwei Zhai, Guotai Wang, Xiangde Luo +3
The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-int…
Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos
Yuchen Wang, Zhongyu Li, Xiangxiang Cui +4
Ultrasound examination is widely used in the clinical diagnosis of thyroid nodules (benign/malignant). However, the accuracy relies heavily on radiologist experience. Although deep…
Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision
Xiangde Luo, Minhao Hu, Wenjun Liao +4
Medical image segmentation plays an irreplaceable role in computer-assisted diagnosis, treatment planning, and following-up. Collecting and annotating a large-scale dataset is cruc…
Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping
Yunxiang Li, Ruilong Dan, Shuai Wang +8
Skull stripping is a crucial prerequisite step in the analysis of brain magnetic resonance images (MRI). Although many excellent works or tools have been proposed, they suffer from…
Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer
Xiangde Luo, Minhao Hu, Tao Song +2
Recently, deep learning with Convolutional Neural Networks (CNNs) and Transformers has shown encouraging results in fully supervised medical image segmentation. However, it is stil…
Few-Shot Domain Adaptation with Polymorphic Transformers
Shaohua Li, Xiuchao Sui, Jie Fu +7
Deep neural networks (DNNs) trained on one set of medical images often experience severe performance drop on unseen test images, due to various domain discrepancy between the train…
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
Shaohua Li, Xiuchao Sui, Xiangde Luo +3
Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn ima…