109 citations · 124 across the 17 of their papers we have counts for
13 papers · 1 filter
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation
Qinghe Ma, Jian Zhang, Lei Qi +3
Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods…
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation
Qinghe Ma, Jian Zhang, Zekun Li +3
Large pretrained visual foundation models exhibit impressive general capabilities. However, the extensive prior knowledge inherent in these models can sometimes be a double-edged s…
Dual-Teacher Ensemble Models with Double-Copy-Paste for 3D Semi-Supervised Medical Image Segmentation
Zhan Fa, Shumeng Li, Jian Zhang +3
Semi-supervised learning (SSL) techniques address the high labeling costs in 3D medical image segmentation, with the teacher-student model being a common approach. However, using a…
The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation
Muyang Qiu, Jian Zhang, Lei Qi +3
Despite the recent success of domain generalization in medical image segmentation, voxel-wise annotation for all source domains remains a huge burden. Semi-supervised domain genera…
AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking
Wenxuan Li, Chongyu Qu, Xiaoxi Chen +18
We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and f…
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
Qinghe Ma, Jian Zhang, Lei Qi +3
Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods…
Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
Shumeng Li, Lei Qi, Qian Yu +3
Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce th…
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport
Bin Li, Ye Shi, Qian Yu +1
Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images sharing the same category across diverse domains without relying on labeled data. Prior approaches have typ…
3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks
Heng Cai, Lei Qi, Qian Yu +2
Medical image segmentation typically necessitates a large and precisely annotated dataset. However, obtaining pixel-wise annotation is a labor-intensive task that requires signific…
Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation
Heng Cai, Shumeng Li, Lei Qi +3
Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes i…
Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data
Qianbi Yu, Dongnan Liu, Chaoyi Zhang +2
Deep learning-based segmentation methods have been widely employed for automatic glaucoma diagnosis and prognosis. In practice, fundus images obtained by different fundus cameras v…
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
Yinghuan Shi, Jian Zhang, Tong Ling +5
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel…
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images
Qian Yu, Yinghuan Shi, Jinquan Sun +3
Due to the irregular motion, similar appearance and diverse shape, accurate segmentation of kidney tumor in CT images is a difficult and challenging task. To this end, we present a…