计算机科学
人工智能
机器学习
肺癌
利用
嵌入
模式识别(心理学)
数据挖掘
医学
肿瘤科
计算机安全
作者
Lu Zhao,Ronghui Hou,Wangyuan Zhao,Lu Qiu,Haohua Teng,Yuchen Han,Xiaolong Fu,Jun Zhao
标识
DOI:10.1109/isbi53787.2023.10230825
摘要
Accurate overall survival (OS) prediction for lung cancer patients is of great significance, and the histopathology slides are considered the gold standard for cancer diagnosis and prognosis. However, the current methods usually lack extracting effective features and ignore the utilization of spatial information. To address these challenges, we propose a self-supervised learning guided transformer framework (SET) for OS prediction with whole slide images (WSIs). We introduce self-supervised learning to exploit the characteristics of pathological images and thus capture domain-specific contextual representations. Furthermore, we design a dual-stream position embedding architecture to facilitate aggregating global spatial information. The experimental results on the lung cancer dataset of stage III-N2 demonstrate that our proposed algorithm can achieve a better concordance index compared with state-of-the-art methods. Moreover, the proposed method can significantly divide patients into high-risk group and low-risk group to assist the personalized treatment.
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