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.
1 code implementation • 15 Apr 2024 • Xiao Zhou, Xiaoman Zhang, Chaoyi Wu, Ya zhang, Weidi Xie, Yanfeng Wang
In this paper, we consider the problem of visual representation learning for computational pathology, by exploiting large-scale image-text pairs gathered from public resources, along with the domain specific knowledge in pathology.
1 code implementation • 21 Feb 2024 • Pengcheng Qiu, Chaoyi Wu, Xiaoman Zhang, Weixiong Lin, Haicheng Wang, Ya zhang, Yanfeng Wang, Weidi Xie
The development of open-source, multilingual medical language models can benefit a wide, linguistically diverse audience from different regions.
no code implementations • 28 Dec 2023 • Ziheng Zhao, Yao Zhang, Chaoyi Wu, Xiaoman Zhang, Ya zhang, Yanfeng Wang, Weidi Xie
In this study, we focus on building up a model that aims to Segment Anything in medical scenarios, driven by Text prompts, termed as SAT.
1 code implementation • 26 Dec 2023 • Qiaoyu Zheng, Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Ya zhang, Yanfeng Wang, Weidi Xie
In this study, we aim to investigate the problem of large-scale, large-vocabulary disease classification for radiologic images, which can be formulated as a multi-modal, multi-anatomy, multi-label, long-tailed classification.
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.
1 code implementation • 13 Sep 2023 • Jiayu Lei, Lisong Dai, Haoyun Jiang, Chaoyi Wu, Xiaoman Zhang, Yao Zhang, Jiangchao Yao, Weidi Xie, Yanyong Zhang, Yuehua Li, Ya zhang, Yanfeng Wang
Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed.
1 code implementation • 4 Aug 2023 • Chaoyi Wu, Xiaoman Zhang, Ya zhang, Yanfeng Wang, Weidi Xie
In this study, we aim to initiate the development of Radiology Foundation Model, termed as RadFM.
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 • 27 Apr 2023 • Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya zhang, Yanfeng Wang, Weidi Xie
Our contributions are threefold: (i) we systematically investigate the process of adapting a general-purpose foundation language model towards medical domain, this involves data-centric knowledge injection through the integration of 4. 8M biomedical academic papers and 30K medical textbooks, as well as comprehensive fine-tuning for alignment with domain-specific instructions; (ii) we contribute a large-scale, comprehensive dataset for instruction tuning.
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 • 27 Feb 2023 • Xiaoman Zhang, Chaoyi Wu, Ya zhang, Yanfeng Wang, Weidi Xie
While multi-modal foundation models pre-trained on large-scale data have been successful in natural language understanding and vision recognition, their use in medical domains is still limited due to the fine-grained nature of medical tasks and the high demand for domain knowledge.
no code implementations • 22 Feb 2023 • Chaoyi Wu, Xiaoman Zhang, Yanfeng Wang, Ya zhang, Weidi Xie
In this paper, we consider the problem of disease diagnosis.
no code implementations • 5 Jan 2023 • Chaoyi Wu, Xiaoman Zhang, Ya zhang, Yanfeng Wang, Weidi Xie
In this paper, we consider enhancing medical visual-language pre-training (VLP) with domain-specific knowledge, by exploiting the paired image-text reports from the radiological daily practice.
no code implementations • ICCV 2023 • Chaoyi Wu, Xiaoman Zhang, Ya zhang, Yanfeng Wang, Weidi Xie
In this paper, we consider enhancing medical visual-language pre-training (VLP) with domain-specific knowledge, by exploiting the paired image-text reports from the radiological daily practice.
no code implementations • 7 Sep 2021 • Xiaoman Zhang, Weidi Xie, Chaoqin Huang, Yanfeng Wang, Ya zhang, Xin Chen, Qi Tian
In this paper, we target self-supervised representation learning for zero-shot tumor segmentation.
no code implementations • 5 Aug 2021 • Shixiang Feng, YuHang Zhou, Xiaoman Zhang, Ya zhang, Yanfeng Wang
A novel Multi-teacher Single-student Knowledge Distillation (MS-KD) framework is proposed, where the teacher models are pre-trained single-organ segmentation networks, and the student model is a multi-organ segmentation network.
no code implementations • 9 Mar 2021 • YuHang Zhou, Xiaoman Zhang, Shixiang Feng, Ya zhang, Yanfeng
Specifically, given a pretrained $K$ organ segmentation model and a new single-organ dataset, we train a unified $K+1$ organ segmentation model without accessing any data belonging to the previous training stages.
no code implementations • 13 Oct 2020 • Xiaoman Zhang, Shixiang Feng, YuHang Zhou, Ya zhang, Yanfeng Wang
We demonstrate the effectiveness of our methods on two downstream tasks: i) Brain tumor segmentation, ii) Pancreas tumor segmentation.
no code implementations • 7 May 2019 • Xiaoman Zhang, Ziyuan Zhao, Cen Chen, Songyou Peng, Min Wu, Zhongyao Cheng, Singee Teo, Le Zhang, Zeng Zeng
In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
1 code implementation • 12 Mar 2019 • Ziyuan Zhao, Xiaoman Zhang, Cen Chen, Wei Li, Songyou Peng, Jie Wang, Xulei Yang, Le Zhang, Zeng Zeng
Segmentation stands at the forefront of many high-level vision tasks.