35 citations · 63 across the 9 of their papers we have counts for
9 papers
URL: Combating Label Noise for Lung Nodule Malignancy Grading
Xianze Ai, Zehui Liao, Yong Xia
Due to the complexity of annotation and inter-annotator variability, most lung nodule malignancy grading datasets contain label noise, which inevitably degrades the performance and…
SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation
Wenxi Yue, Jing Zhang, Kun Hu +3
The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is t…
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
Nicholas Heller, Fabian Isensee, Dasha Trofimova +42
This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical…
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…
SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inference
Xiaoyu Bai, Yong Xia
Medical images like CT and MRI provide detailed information about the internal structure of the body, and identifying key anatomical structures from these images plays a crucial ro…
Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
Shishuai Hu, Zehui Liao, Yong Xia
Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adapt…
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…
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…