范畴变量
计算机科学
串联(数学)
人工智能
利用
机器学习
模式识别(心理学)
深度学习
特征(语言学)
双雷达
病变
临床实习
乳腺摄影术
数据挖掘
医学
乳腺癌
病理
数学
家庭医学
哲学
内科学
癌症
组合数学
语言学
计算机安全
作者
Hung Q. Vo,Pengyu Yuan,Tingchao He,Stephen T.C. Wong,Hien Van Nguyen
标识
DOI:10.1109/bhi50953.2021.9508604
摘要
Accurate breast lesion risk estimation can significantly reduce unnecessary biopsies and help doctors decide optimal treatment plans. Most existing computer-aided systems rely solely on mammogram features to classify breast lesions. While this approach is convenient, it does not fully exploit useful information in clinical reports to achieve the optimal performance. Would clinical features significantly improve breast lesion classification compared to using mammograms alone? How to handle missing clinical information caused by variation in medical practice? What is the best way to combine mammograms and clinical features? There is a compelling need for a systematic study to address these fundamental questions. This paper investigates several multimodal deep networks based on feature concatenation, cross-attention, and co-attention to combine mammograms and categorical clinical variables. We show that the proposed architectures significantly increase the lesion classification performance (average area under ROC curves from 0.89 to 0.94). We also evaluate the model when clinical variables are missing.
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