通过视觉语义矛盾意识网络和焦点自我批评序列培训生成放射学报告
Xiulong Yi1, You Fu1, Enxu Bi2
1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China.
概括
本研究引入了一种新的视觉语义矛盾意识网络 (VSANet) 和焦点自我批判序列训练 (FSCST),通过解决深度学习模型中的数据偏差来改善放射学报告生成.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 自然语言处理自然语言处理.
背景情况:
- 放射学报告生成的深度学习模型与数据偏差作斗争,影响异常的准确描述.
- 由于数据集偏差,现有的方法在捕获和描述微妙或罕见的发现方面面临挑战.
研究的目的:
- 开发一个先进的深度学习框架,VSANet与FSCST,以减轻放射学报告生成中的数据偏差.
- 提高自动化放射学报告的准确性和完整性,特别是异常发现.
主要方法:
- 拟议的视觉语义矛盾意识网络 (VSANet) 采用编码器解码器架构.
- 使用参数共享双向编码器 (PSDwE) 进行特征提取,并使用视觉语义矛盾意识 (VSA) 模块进行偏差缓解.
- 实施焦点自我批评序列培训 (FSCST) 以专注于具有挑战性的样本.
主要成果:
- VSANet在各种评估指标上生成准确的放射学报告方面表现出卓越的表现.
- 通过生成异常感知视觉特征,VSA模块有效地减轻了数据偏差.
- FSCST进一步提高了该模型处理困难案件的能力.
结论:
- 拟议的VSANet和FSCST为改进基于深度学习的放射学报告生成提供了强大的解决方案.
- 这种方法显示出克服医疗AI数据偏差挑战的巨大潜力.
- 该框架有助于更可靠,更准确的自动化医疗报告.
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