3 citations · 6 across the 5 of their papers we have counts for
5 papers
HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images
Yuchen Yang, Haoran Yan, Yanhao Chen +2
Vision Question Answering (VQA) tasks use images to convey critical information to answer text-based questions, which is one of the most common forms of question answering in real-…
ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation
Zihan Li, Yuan Zheng, Dandan Shan +6
Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally ca…
ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding
Zihan Li, Yuan Zheng, Xiangde Luo +2
Medical image segmentation plays a critical role in clinical decision-making, treatment planning, and disease monitoring. However, accurate segmentation of medical images is challe…
Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment
Dandan Shan, Zihan Li, Wentao Chen +3
Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19. However, there are few relevant studies due to the lack of detailed informatio…
TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation
Zihan Li, Dihan Li, Cangbai Xu +4
Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-b…