2 citations · 2 across the 9 of their papers we have counts for
9 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…
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…