对比了三种增强方法与焦点损失在脑肿瘤分割中的效果。
Reproducible Evaluation of Data Augmentation and Loss Functions for Brain Tumor Segmentation
- 用U-Net结合焦点损失和数据增强提升分割性能。
- 焦点损失使精度达90%，媲美顶尖水平。
- 代码与结果公开，便于复现与后续研究。
脑肿瘤分割对诊断与治疗规划至关重要，但类别不平衡和模型泛化能力不足仍制约进展。本文在公开的MRI数据集上，对U-Net分割模型在脑肿瘤影像中使用焦点损失与基础数据增强策略进行了可复现评估。实验聚焦于焦点损失参数调优，并分析了三种数据增强技术的影响：水平翻转、旋转与缩放。采用焦点损失的U-Net模型达到90%的精度，与当前最优结果相当。通过公开全部代码与结果，本研究建立了透明、可复现的基线，为未来增强策略与损失函数设计提供参考。
原文摘要 · Abstract (English)
Brain tumor segmentation is crucial for diagnosis and treatment planning, yet challenges such as class imbalance and limited model generalization continue to hinder progress. This work presents a reproducible evaluation of U-Net segmentation performance on brain tumor MRI using focal loss and basic data augmentation strategies. Experiments were conducted on a publicly available MRI dataset, focusing on focal loss parameter tuning and assessing the impact of three data augmentation techniques: horizontal flip, rotation, and scaling. The U-Net with focal loss achieved a precision of 90%, comparable to state-of-the-art results. By making all code and results publicly available, this study establishes a transparent, reproducible baseline to guide future research on augmentation strategies and loss function design in brain tumor segmentation.
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。