89 citations · 249 across the 24 of their papers we have counts for
24 papers
KANResDiff: Learning Local Residual Diffusion via Kolmogorov-Arnold Network for Ambiguous Medical Image Segmentation
Fanding Li, Chenglin Wang, Xiangyu Li +9
Ambiguous medical image segmentation aims to provide a series of diverse but plausible segmentation hypotheses. However, existing methods introduce stochasticity in a fixed and pre…
TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
Marawan Elbatel, Mohamed Ghonim, Jiaji Mao +62
Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings acro…
PCA-Enhanced Probabilistic U-Net for Effective Ambiguous Medical Image Segmentation
Xiangyu Li, Chenglin Wang, Qiantong Shen +6
Ambiguous Medical Image Segmentation (AMIS) is significant to address the challenges of inherent uncertainties from image ambiguities, noise, and subjective annotations. Existing c…
Fully Kolmogorov-Arnold Deep Model in Medical Image Segmentation
Xingyu Qiu, Xinghua Ma, Dong Liang +4
Deeply stacked KANs are practically impossible due to high training difficulties and substantial memory requirements. Consequently, existing studies can only incorporate few KAN la…
Ambiguity-aware Truncated Flow Matching for Ambiguous Medical Image Segmentation
Fanding Li, Xiangyu Li, Xianghe Su +6
A simultaneous enhancement of accuracy and diversity of predictions remains a challenge in ambiguous medical image segmentation (AMIS) due to the inherent trade-offs. While truncat…
Structure and Smoothness Constrained Dual Networks for MR Bias Field Correction
Dong Liang, Xingyu Qiu, Yuzhen Li +4
MR imaging techniques are of great benefit to disease diagnosis. However, due to the limitation of MR devices, significant intensity inhomogeneity often exists in imaging results,…
Elucidating the Design Space of Arbitrary-Noise-Based Diffusion Models
Xingyu Qiu, Mengying Yang, Xinghua Ma +6
Although EDM aims to unify the design space of diffusion models, its reliance on fixed Gaussian noise prevents it from explaining emerging flow-based methods that diffuse arbitrary…
Finding Local Diffusion Schrödinger Bridge using Kolmogorov-Arnold Network
Xingyu Qiu, Mengying Yang, Xinghua Ma +6
In image generation, Schrödinger Bridge (SB)-based methods theoretically enhance the efficiency and quality compared to the diffusion models by finding the least costly path betwee…
Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge
Gongning Luo, Mingwang Xu, Hongyu Chen +34
Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a ne…
MedFILIP: Medical Fine-grained Language-Image Pre-training
Xinjie Liang, Xiangyu Li, Fanding Li +7
Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to acc…
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences
Yuetan Chu, Gongning Luo, Longxi Zhou +13
Pulmonary artery-vein segmentation is crucial for disease diagnosis and surgical planning and is traditionally achieved by Computed Tomography Pulmonary Angiography (CTPA). However…
Unsupervised Decomposition Networks for Bias Field Correction in MR Image
Dong Liang, Xingyu Qiu, Kuanquan Wang +3
Bias field, which is caused by imperfect MR devices or imaged objects, introduces intensity inhomogeneity into MR images and degrades the performance of MR image analysis methods.…
Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge
Gongning Luo, Kuanquan Wang, Jun Liu +27
Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most exist…
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
Xiangyu Li, Gongning Luo, Kuanquan Wang +31
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores…
Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation
Dong Liang, Jun Liu, Kuanquan Wang +3
The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic…
ULTRA: Uncertainty-aware Label Distribution Learning for Breast Tumor Cellularity Assessment
Xiangyu Li, Xinjie Liang, Gongning Luo +3
Neoadjuvant therapy (NAT) for breast cancer is a common treatment option in clinical practice. Tumor cellularity (TC), which represents the percentage of invasive tumors in the tum…
Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries
Xinghua Ma, Gongning Luo, Wei Wang +1
Coronary artery disease (CAD) has posed a leading threat to the lives of cardiovascular disease patients worldwide for a long time. Therefore, automated diagnosis of CAD has indisp…
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
Vivek Singh Bawa, Gurkirt Singh, Francis KapingA +16
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging. The challenges come from the peculiar struct…
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
Xiahai Zhuang, Jiahang Xu, Xinzhe Luo +20
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suff…
Multi-step Cascaded Networks for Brain Tumor Segmentation
Xiangyu Li, Gongning Luo, Kuanquan Wang
Automatic brain tumor segmentation method plays an extremely important role in the whole process of brain tumor diagnosis and treatment. In this paper, we propose a multi-step casc…
An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image
Yashu Liu, Wei Wang, Kuanquan Wang +2
LGE CMR is an efficient technology for detecting infarcted myocardium. An efficient and objective ventricle segmentation method in LGE can benefit the location of the infarcted myo…
VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination
Suyu Dong, Gongning Luo, Kuanquan Wang +5
3D left ventricle (LV) segmentation on echocardiography is very important for diagnosis and treatment of cardiac disease. It is not only because of that echocardiography is a real-…
Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images
Gongning Luo, Suyu Dong, Kuanquan Wang +3
Left ventricular (LV) volumes estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address direct LV volumes prediction task. Methods…