no code implementations • 25 Apr 2024 • Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao, Jiayu Lei, Ya zhang, Yanfeng Wang, Weidi Xie
We believe that RadGenome-Chest CT can significantly advance the development of multimodal medical foundation models, by training to generate texts based on given segmentation regions, which is unattainable with previous relevant datasets.
no code implementations • 28 Dec 2023 • Ziheng Zhao, Yao Zhang, Chaoyi Wu, Xiaoman Zhang, Ya zhang, Yanfeng Wang, Weidi Xie
Our main contributions are three folds: (i) on data construction, we combine multiple knowledge sources to construct a multi-modal medical knowledge tree; Then we build up a large-scale segmentation dataset for training, by collecting over 11K 3D medical image scans from 31 segmentation datasets with careful standardization on both visual scans and label space; (ii) on model training, we formulate a universal segmentation model, that can be prompted by inputting medical terminologies in text form.
1 code implementation • 15 Oct 2023 • Chaoyi Wu, Jiayu Lei, Qiaoyu Zheng, Weike Zhao, Weixiong Lin, Xiaoman Zhang, Xiao Zhou, Ziheng Zhao, Ya zhang, Yanfeng Wang, Weidi Xie
Driven by the large foundation models, the development of artificial intelligence has witnessed tremendous progress lately, leading to a surge of general interest from the public.
2 code implementations • 17 May 2023 • Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao, Weixiong Lin, Ya zhang, Yanfeng Wang, Weidi Xie
In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA), which is crucial in efficiently interpreting medical images with vital clinic-relevant information.
Ranked #1 on Medical Visual Question Answering on PMC-VQA
1 code implementation • 13 Mar 2023 • Weixiong Lin, Ziheng Zhao, Xiaoman Zhang, Chaoyi Wu, Ya zhang, Yanfeng Wang, Weidi Xie
Foundation models trained on large-scale dataset gain a recent surge in CV and NLP.
Ranked #3 on Medical Visual Question Answering on PMC-VQA
1 code implementation • 14 Jun 2022 • Ziheng Zhao, Tianjiao Zhang, Weidi Xie, Yanfeng Wang, Ya zhang
This paper considers the problem of undersampled MRI reconstruction.