no code implementations • 18 Mar 2024 • Runtian Yuan, Qingqiu Li, Junlin Hou, Jilan Xu, Yuejie Zhang, Rui Feng, Hao Chen
In response to the need for rapid and accurate COVID-19 diagnosis during the global pandemic, we present a two-stage framework that leverages pseudo labels for domain adaptation to enhance the detection of COVID-19 from CT scans.
no code implementations • 18 Mar 2024 • Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu, Yuejie Zhang, Rui Feng, Hao Chen
To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model.
no code implementations • 14 Mar 2024 • Qingqiu Li, Xiaohan Yan, Jilan Xu, Runtian Yuan, Yuejie Zhang, Rui Feng, Quanli Shen, Xiaobo Zhang, Shujun Wang
For finding and existence, we regard them as image tags, applying an image-tag recognition decoder to associate image features with their respective tags within each sample and constructing soft labels for contrastive learning to improve the semantic association of different image-report pairs.
no code implementations • 1 Nov 2023 • Qingqiu Li, Jilan Xu, Runtian Yuan, Mohan Chen, Yuejie Zhang, Rui Feng, Xiaobo Zhang, Shang Gao
Automatic generation of radiology reports holds crucial clinical value, as it can alleviate substantial workload on radiologists and remind less experienced ones of potential anomalies.