深度神经网络在医学图像分割中实现精准诊断，助力癌症早发现。
A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks
- 基于DNN的医学图像分割技术提升诊断精度。
- 结合可解释AI实现从智能到智慧的跨越。
- 适合医疗AI研究者与临床医生参考。
过去十年，基于深度神经网络（DNN）的医学图像分割（MIS）取得了显著性能提升，具有广阔发展前景。本文对基于DNN的MIS进行系统研究，聚焦于智能视觉系统在数据、信息、知识、智能与智慧（DIKIW）各层级的前沿成果。随着可解释人工智能（XAI）成为重要方向，研究致力于揭示传统DNN架构的“黑箱”特性，以满足透明性与伦理要求。论文强调了MIS在疾病诊断与早期检测中的关键作用，尤其对通过及时诊断提高癌症患者生存率意义重大。XAI与早期预测被视为从‘智能’迈向‘智慧’的两个关键步骤。同时，论文分析了现有挑战，并提出提升DNN-based MIS实施效率的潜在解决方案。
原文摘要 · Abstract (English)
Over the past decade, Medical Image Segmentation (MIS) using Deep Neural Networks (DNNs) has achieved significant performance improvements and holds great promise for future developments. This paper presents a comprehensive study on MIS based on DNNs. Intelligent Vision Systems are often evaluated based on their output levels, such as Data, Information, Knowledge, Intelligence, and Wisdom (DIKIW),and the state-of-the-art solutions in MIS at these levels are the focus of research. Additionally, Explainable Artificial Intelligence (XAI) has become an important research direction, as it aims to uncover the "black box" nature of previous DNN architectures to meet the requirements of transparency and ethics. The study emphasizes the importance of MIS in disease diagnosis and early detection, particularly for increasing the survival rate of cancer patients through timely diagnosis. XAI and early prediction are considered two important steps in the journey from "intelligence" to "wisdom." Additionally, the paper addresses existing challenges and proposes potential solutions to enhance the efficiency of implementing DNN-based MIS.
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