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成果类型:
期刊论文
作者:
Xue, Youyuan;Tan, Yun;Tan, Ling;Qin, Jiaohua;Xiang, Xuyu
通讯作者:
Tan, Y
作者机构:
[Qin, Jiaohua; Tan, Yun; Xiang, Xuyu; Xue, Youyuan] Cent South Univ Forestry & Technol, Coll Comp Sci & Informat Technol, Changsha 410004, Peoples R China.
[Tan, Ling] Cent South Univ, Xiangya Hosp 2, Changsha 410011, Peoples R China.
通讯机构:
[Tan, Y ] C
Cent South Univ Forestry & Technol, Coll Comp Sci & Informat Technol, Changsha 410004, Peoples R China.
语种:
英文
关键词:
Attention mechanism;Auxiliary signal;Memory mechanism;Radiology reports generation
期刊:
Expert Systems with Applications
ISSN:
0957-4174
年:
2024
卷:
237
页码:
121260
基金类别:
CRediT authorship contribution statement Youyuan Xue: Conceptualization, Methodology, Software, Writing – original draft. Yun Tan: Validation, Writing – review & editing, Project administration, acquisition. Ling Tan: Data curation, Investigation. Jiaohua Qin: Supervision. Xuyu Xiang: Software, Visualization.
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
Automatically generating medical image reports is a gratifying task. For doctors, it can reduce the heavy burden of writing reports, and for patients, it can reduce the waiting time for reports; it can also avoid misdiagnosis and missed diagnoses caused by human factors. However, this task still faces enormous challenges due to the problem of visual and textual data bias and the complex relationships among the components of medical reports. To this end, in this work, we propose an auxiliary signal guidance and memory-driven (ASGMD) network that...

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