4 papers
Unveiling and Bridging the Functional Perception Gap in MLLMs: Atomic Visual Alignment and Hierarchical Evaluation via PET-Bench
Zanting Ye, Xiaolong Niu, Xuanbin Wu +14
While Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in tasks such as abnormality detection and report generation for anatomical modalities, thei…
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising
Xiaolong Niu, Zanting Ye, Xu Han +4
Acquiring high-quality Positron Emission Tomography (PET) images requires administering high-dose radiotracers, which increases radiation exposure risks. Generating standard-dose P…
Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentation
Zanting Ye, Xiaolong Niu, Xuanbin Wu +3
Deep learning-based medical image segmentation has shown remarkable success; however, it typically requires extensive pixel-level annotations, which are both expensive and time-int…
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning
Zanting Ye, Xiaolong Niu, Xu Han +7
Organ segmentation in Positron Emission Tomography (PET) plays a vital role in cancer quantification. Low-dose PET (LDPET) provides a safer alternative by reducing radiation exposu…