7 papers
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation
Xiaofeng Liu, Qianru Zhang, Thibault Marin +4
The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PET) is important to oncologic i…
Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection under Label Shift
Xiaofeng Liu, Menghua Xia, Yanis Chemli +3
In this work, we propose an unsupervised domain adaptation (UDA) framework for 3D volumetric lesion detection that adapts a detector trained on labeled FDG PET/CT to unlabeled PSMA…
On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report)
Menghua Xia, Reimund Bayerlein, Yanis Chemli +7
Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software solutions for tasks such as im…
Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging
Menghua Xia, Kuan-Yin Ko, Der-Shiun Wang +12
Positron emission tomography (PET) image denoising, along with lesion and organ segmentation, are critical steps in PET-aided diagnosis. However, existing methods typically treat t…
Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes
Xiaoqi Zhao, Youwei Pang, Shijie Chang +10
As large-scale foundation models trained on billions of image--mask pairs covering a vast diversity of scenes, objects, and contexts, SAM and its upgraded version, SAM~2, have sign…
Dual Prompting for Diverse Count-level PET Denoising
Xiaofeng Liu, Yongsong Huang, Thibault Marin +6
The to-be-denoised positron emission tomography (PET) volumes are inherent with diverse count levels, which imposes challenges for a unified model to tackle varied cases. In this w…