15 citations · 25 across the 20 of their papers we have counts for
5 papers · 1 filter
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?
Yunfei Xie, Juncheng Wu, Haoqin Tu +6
Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest…
MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine
Yunfei Xie, Ce Zhou, Lang Gao +8
This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities with multigranular annotati…
Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt Guidance
Jiawei Mao, Juncheng Wu, Yuyin Zhou +2
There are many excellent solutions in image restoration.However, most methods require on training separate models to restore images with different types of degradation.Although exi…
DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor
Juncheng Wu, Zhangkai Ni, Hanli Wang +3
Image deep features extracted by pre-trained networks are known to contain rich and informative representations. In this paper, we present Deep Degradation Response (DDR), a method…
Misalignment-Robust Frequency Distribution Loss for Image Transformation
Zhangkai Ni, Juncheng Wu, Zian Wang +3
This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely a…