自编码
鳞状细胞癌
情态动词
特征(语言学)
食管癌
食管鳞状细胞癌
癌症
计算机科学
人工智能
医学
内科学
深度学习
化学
语言学
哲学
高分子化学
作者
Chengyu Wu,Yatao Zhang,Yaqi Wang,Qifeng Wang,Shuai Wang
出处
期刊:Cornell University - arXiv
日期:2024-08-23
标识
DOI:10.48550/arxiv.2408.13290
摘要
Survival prediction for esophageal squamous cell cancer (ESCC) is crucial for doctors to assess a patient's condition and tailor treatment plans. The application and development of multi-modal deep learning in this field have attracted attention in recent years. However, the prognostically relevant features between cross-modalities have not been further explored in previous studies, which could hinder the performance of the model. Furthermore, the inherent semantic gap between different modal feature representations is also ignored. In this work, we propose a novel autoencoder-based deep learning model to predict the overall survival of the ESCC. Two novel modules were designed for multi-modal prognosis-related feature reinforcement and modeling ability enhancement. In addition, a novel joint loss was proposed to make the multi-modal feature representations more aligned. Comparison and ablation experiments demonstrated that our model can achieve satisfactory results in terms of discriminative ability, risk stratification, and the effectiveness of the proposed modules.
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