139 citations · 312 across the 27 of their papers we have counts for
27 papers
Super-Generalist: Towards Comprehensive and Accurate Medical Image Understanding via Generalist-Specialist Synergy
Shaoteng Zhang, Weiwei Cao, Wanxing Chang +9
Medical images require comprehensive and accurate interpretation to support the diagnosis of diverse clincial conditions. Recent vision-language generalist models offer broad task…
Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentation
Truong Vu, Minh Khoi Ho, Yutong Xie
Few-shot medical image segmentation methods typically assume a single ground-truth annotation, overlooking systematic variability across expert raters commonly observed in clinical…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model
Yiwen Ye, Ziyang Chen, Jianpeng Zhang +2
Universal segmentation models offer significant potential in addressing a wide range of tasks by effectively leveraging discrete annotations. As the scope of tasks and modalities e…
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?
Yutong Xie, Qi Chen, Sinuo Wang +7
Current vision-language pre-training (VLP) methodologies predominantly depend on paired image-text datasets, a resource that is challenging to acquire in radiology due to privacy c…
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning
Yiwen Ye, Yutong Xie, Jianpeng Zhang +3
Self-supervised learning is an efficient pre-training method for medical image analysis. However, current research is mostly confined to specific-modality data pre-training, consum…
Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation
Qingjie Zeng, Yutong Xie, Zilin Lu +3
Annotation scarcity has become a major obstacle for training powerful deep-learning models for medical image segmentation, restricting their deployment in clinical scenarios. To ad…
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation
Qingjie Zeng, Yutong Xie, Zilin Lu +2
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation. Unlike p…
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Biao Wu, Yutong Xie, Zeyu Zhang +6
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing a…
Transformer-based Annotation Bias-aware Medical Image Segmentation
Zehui Liao, Yutong Xie, Shishuai Hu +1
Manual medical image segmentation is subjective and suffers from annotator-related bias, which can be mimicked or amplified by deep learning methods. Recently, researchers have sug…
Attention Mechanisms in Medical Image Segmentation: A Survey
Yutong Xie, Bing Yang, Qingbiao Guan +3
Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in…
Act Like a Radiologist: Radiology Report Generation across Anatomical Regions
Qi Chen, Yutong Xie, Biao Wu +5
Automating radiology report generation can ease the reporting workload for radiologists. However, existing works focus mainly on the chest area due to the limited availability of p…
Unsupervised Image Denoising with Score Function
Yutong Xie, Mingze Yuan, Bin Dong +1
Though achieving excellent performance in some cases, current unsupervised learning methods for single image denoising usually have constraints in applications. In this paper, we p…
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner
Yiwen Ye, Yutong Xie, Jianpeng Zhang +2
The universal model emerges as a promising trend for medical image segmentation, paving up the way to build medical imaging large model (MILM). One popular strategy to build univer…
Instance-dependent Label Distribution Estimation for Learning with Label Noise
Zehui Liao, Shishuai Hu, Yutong Xie +1
Noise transition matrix (NTM) estimation is a promising approach for learning with label noise. It can infer clean posterior probabilities, known as Label Distribution (LD), based…
Learning from partially labeled data for multi-organ and tumor segmentation
Yutong Xie, Jianpeng Zhang, Yong Xia +1
Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream app…
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers
Yutong Xie, Jianpeng Zhang, Yong Xia +2
Although Transformers have successfully transitioned from their language modelling origins to image-based applications, their quadratic computational complexity remains a challenge…
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier
Yutong Xie, Jianpeng Zhang, Yong Xia +1
Self-supervised learning (SSL) opens up huge opportunities for medical image analysis that is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D me…
Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation
Zehui Liao, Shishuai Hu, Yutong Xie +1
Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. Deep learning models may surpass human performance on a variety of tasks…
Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction
Zehui Liao, Yutong Xie, Shishuai Hu +1
Lung nodule malignancy prediction is an essential step in the early diagnosis of lung cancer. Besides the difficulties commonly discussed, the challenges of this task also come fro…
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
Yutong Xie, Jianpeng Zhang, Chunhua Shen +1
Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. The convolutional operations used in these networks, however, inevi…
Inter-slice Context Residual Learning for 3D Medical Image Segmentation
Jianpeng Zhang, Yutong Xie, Yan Wang +1
Automated and accurate 3D medical image segmentation plays an essential role in assisting medical professionals to evaluate disease progresses and make fast therapeutic schedules.…
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation
Yutong Xie, Jianpeng Zhang, Zehui Liao +2
It has been widely recognized that the success of deep learning in image segmentation relies overwhelmingly on a myriad amount of densely annotated training data, which, however, a…
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
Jianpeng Zhang, Yutong Xie, Yong Xia +1
Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel level, most benchmark datasets are equipped with the annotations of only one type of org…