7 papers
3D Consistency Optimization for Self-Supervised Monocular Video Depth Estimation
Yuanye Liu, Ke Zhang, Junzhe Jiang +3
Reliable monocular video depth estimation is crucial for downstream 3D reasoning and embodied AI in endoscopic navigation. However, existing self-supervised approaches typically tr…
Generalized Evidential Deep Learning: From a Bayesian Perspective
Yuanye Liu, Yibo Gao, Yuanyang Chen +1
Evidential Deep Learning (EDL) has emerged as an efficient, sampling-free strategy for uncertainty estimation. A series of EDL variants have been proposed to address specific limit…
How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark
Yuanye Liu, Nannan Shi, Zhejia Zhang +20
Despite years of methodological progress, how far AI has come in liver fibrosis staging has never been systematically evaluated under the heterogeneous, multi-center conditions tha…
X-Edit: Exact, Explicit, and Explainable Null-Space Editing for Medical Vision Transformers
Yuanye Liu, Siyuan Zhou, Ke Zhang +3
Pre-trained Vision Transformers (ViTs) are increasingly deployed for medical image classification. However, correcting their inevitable failure cases in dynamic clinical scenarios…
On-Policy Distillation with Best-of-N Teacher Rollout Selection
Ke Zhang, Yunjie Tian, Dongdi Zhao +4
On-policy distillation (OPD), which supervises a student on its own sampled trajectories, has emerged as a data-efficient post-training method for improving reasoning while avoidin…
Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method
Yuanye Liu, Hanxiao Zhang, Jiyao Liu +5
Liver fibrosis represents a significant global health burden, necessitating accurate staging for effective clinical management. This report introduces the LiQA (Liver Fibrosis Quan…