机器学习在结直肠癌中的应用进展

Progress in Machine Learning Applications for Colorectal Cancer

摘要: 近年来，机器学习技术在医学影像分析领域展现出突破性潜力，特别是在结直肠癌的早期筛查、病理诊断和预后评估等方面。现有研究表明，集成学习方法通过融合多模态数据显著提升了肿瘤分期的准确率，而迁移学习策略则有效缓解了医学影像样本稀缺的瓶颈问题。然而，当前研究仍面临模型可解释性不足、小样本学习效果欠佳等挑战，特别是在处理异质性肿瘤组织时的泛化能力有待加强。未来研究应着重探索自监督学习在医学图像表征学习中的应用潜力，开发基于注意力机制的多尺度特征融合架构，并建立标准化的跨中心验证框架。从临床应用角度看，需要进一步优化模型的计算效率，完善人机协同决策机制，以实现人工智能辅助诊断系统向临床实践的平稳过渡。

Abstract: In recent years, machine learning technology has demonstrated groundbreaking potential in the field of medical image analysis, particularly in the early screening, pathological diagnosis, and prognosis assessment of colorectal cancer. Existing studies have shown that ensemble learning methods significantly improve the accuracy of tumor staging by integrating multimodal data, while transfer learning strategies effectively address the bottleneck problem of scarce medical image samples. However, current research still faces challenges such as insufficient model interpretability and poor performance in small sample learning, especially in terms of generalization ability when dealing with heterogeneous tumor tissues. Future research should focus on exploring the application potential of self-supervised learning in medical image representation learning, developing multi-scale feature fusion architectures based on attention mechanisms, and establishing standardized cross-center validation frameworks. From a clinical application perspective, it is necessary to further optimize the computational efficiency of the model, improve the human-machine collaborative decision-making mechanism, and achieve a smooth transition of artificial intelligence-assisted diagnostic systems to clinical practice.