# nnU-Net 在 BraTS-GoAT 2026 脑肿瘤人群中的泛化能力评估

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-14 08:00
- AIHOT 分数：30
- AIHOT 链接：https://aihot.news/items/cmu5fhe6m07f4ro7norbshn9m
- 原文链接：https://arxiv.org/abs/2609.15524

## AI 摘要

研究在 BraTS-GoAT 2026 上评估 3D nnU-Net 的跨人群肿瘤分割泛化性，用 1,351 例标注数据做五折交叉验证、每折训练 1,000 epochs。

## 正文

BraTS-GoAT evaluates tumor segmentation across heterogeneous populations. We trained a conventional 3D nnU-Net on 1,351 labeled cases using five-fold cross-validation and 1,000 epochs per fold. The final predictor averaged all folds and applied test-time mirroring. On pooled official validation, global DSC values were 0.7805, 0.8288, and 0.8854 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Under matched fold-0 inference, mean regional Dice decreased from 0.9058 on source out-of-fold (OOF) cases to 0.8310 on pooled validation (difference--0.0747). Mirroring gave small single-fold gains but no clear ensemble benefit; a residual-encoder alternative reached 0.8282 mean Dice. In labeled OOF predictions, failure cases had substantially smaller reference ET volumes; after adjustment for ET and WT volume, lower Dice remained associated with more disconnected ET components and a smaller fraction of ET contained in the largest component.