基于深度学习的医学多模态数据融合方法在肿瘤学中的进展和挑战

Progress and Challenges of Medical Multimodal Data Fusion Methods Based on Deep Learning in Oncology

【目的】在肿瘤学中,患者有一系列的临床数据,从放射学、组织学、基因组学到电子健康记录。不同数据模式的整合为提高诊断和预后模型的稳健性和准确性提供了机会,使人工智能在临床实践发挥重要作用。【方法】本文将探讨深度学习技术以及其在肿瘤医学数据中的应用,并研究肿瘤学领域多模态数据融合方法的潜在影响和重要发现。【结果】多模态数据能够更好地发现与患者治疗响应、预后效果相关的信息,从而构建更加鲁棒的深度学习模型。【结论】深度学习已经在医学领域取得了显著的进展,特别是在肿瘤学研究中处理多模态医学数据。这些进展为临床提供了更准确、更快速的工具来进行肿瘤的检测、分割、分类和预后预测,同时也面临很多挑战亟须解决。

[Objective]In oncology,patients have a range of clinical data spanning radiology,histology,genomics,and electronic health records.Integrating diverse data modalities presents an opportunity to enhance the robustness and accuracy of diagnostic and prognostic models,enabling artificial intelligence to play a crucial role in clinical practice.[Methods]This article explores the techniques of deep learning and its application in oncology data,as well as investigates the potential impact and essential findings of multimodal data fusion methods in the field of oncology.[Results]Multimodal data can better uncover information related to patient treatment responses and predictive outcomes,thus constructing more robust deep learning models.[Conclusions]Deep learning has achieved significant advance in the medical field,particularly in handling multimodal medical data in oncology research.These advancements provide clinical practitioners with more accurate and faster tools for tumour detection,segmentation,classification,and prognosis prediction.At the same time,many challenges need to be solved.