4 citations · 4 across the 5 of their papers we have counts for
8 papers
DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching
Zicheng Xu, Xiuyi Lou, Guanchu Wang +6
Large Reasoning Models (LRMs) achieve remarkable inference-time improvements through parallel thinking. However, existing methods rely on redundant sampling of reasoning trajectori…
Self-ensemble: Mitigating Confidence Mis-calibration for Large Language Models
Zicheng Xu, Guanchu Wang, Guangyao Zheng +4
Although Large Language Models (LLMs) perform well in general fields, they exhibit a confidence distortion problem on multi-choice question-answering (MCQA), particularly as the nu…
Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings
Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman +1
Self-supervised learning has revolutionized medical imaging by enabling efficient and generalizable feature extraction from large-scale unlabeled datasets. Recently, self-supervise…
Demographic Predictability in 3D CT Foundation Embeddings
Guangyao Zheng, Michael A. Jacobs, Vishwa S. Parekh
Self-supervised foundation models have recently been successfully extended to encode three-dimensional (3D) computed tomography (CT) images, with excellent performance across sever…
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
Guangyao Zheng, Shuhao Lai, Vladimir Braverman +2
While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs. Since imaging environmen…
Multi-environment lifelong deep reinforcement learning for medical imaging
Guangyao Zheng, Shuhao Lai, Vladimir Braverman +2
Deep reinforcement learning(DRL) is increasingly being explored in medical imaging. However, the environments for medical imaging tasks are constantly evolving in terms of imaging…