用动态课程学习提升脑影像时空编码精度
DCL-SE: Dynamic Curriculum Learning for Spatiotemporal Encoding of Brain Imaging
- 基于数据驱动的时空编码，将三维脑数据转为二维动态表征
- 在6个公开数据集上准确率超越现有方法，尤其在病理细节识别上优势明显
- 适合脑疾病诊断、影像分析等需要高精度解码的研究者
针对临床脑影像分析中时空保真度受限及通用大模型适应性不足的问题，本文提出动态课程学习的时空编码框架（DCL-SE），核心为数据驱动的时空编码（DaSE）。通过近似秩池化（ARP）将三维脑结构数据高效编码为信息丰富的二维动态表示，并引入由动态分组机制（DGM）引导的动态课程学习策略，逐步训练解码器，从全局解剖结构到精细病灶特征逐步优化特征提取。在包含阿尔茨海默病与脑肿瘤分类、脑动脉分割、脑龄预测在内的六个公开数据集上评估，DCL-SE在准确率、鲁棒性和可解释性方面均持续优于现有方法。结果表明，在大规模预训练模型时代，紧凑且任务专用的架构仍具关键价值。
原文摘要 · Abstract (English)
High-dimensional neuroimaging analyses for clinical diagnosis are often constrained by compromises in spatiotemporal fidelity and by the limited adaptability of large-scale, general-purpose models. To address these challenges, we introduce Dynamic Curriculum Learning for Spatiotemporal Encoding (DCL-SE), an end-to-end framework centered on data-driven spatiotemporal encoding (DaSE). We leverage Approximate Rank Pooling (ARP) to efficiently encode three-dimensional volumetric brain data into information-rich, two-dimensional dynamic representations, and then employ a dynamic curriculum learning strategy, guided by a Dynamic Group Mechanism (DGM), to progressively train the decoder, refining feature extraction from global anatomical structures to fine pathological details. Evaluated across six publicly available datasets, including Alzheimer's disease and brain tumor classification, cerebral artery segmentation, and brain age prediction, DCL-SE consistently outperforms existing methods in accuracy, robustness, and interpretability. These findings underscore the critical importance of compact, task-specific architectures in the era of large-scale pretrained networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。