11 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…
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…
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…