用可解释的视觉变压器实现脑肿瘤四类自动分类，准确率达99.3%
an interpretable vision transformer framework for automated brain tumor classification
- 基于ViT-B/16架构，结合CLAHE增强和两阶段微调提升特征提取能力
- 测试准确率99.29%，对健康与脑膜瘤类别实现完美召回
- 提供注意力热图解释预测依据，适合临床辅助诊断场景
脑肿瘤是严重神经系统疾病，早期精准诊断直接影响生存率。人工解读磁共振成像（MRI）耗时长、易受主观差异影响，且需专业经验。本文提出一种深度学习框架，用于从7,023张MRI扫描中自动区分胶质瘤、脑膜瘤、垂体瘤及健康脑组织四类。模型采用在ImageNet-21k上预训练的ViT-B/16作为主干网络，结合临床导向的预处理与训练流程：使用对比度受限自适应直方图均衡化（CLAHE）增强局部对比度，突出肿瘤边界；采用两阶段微调策略——先冻结主干微调分类头，再全网微调并使用可变学习率；每批次应用MixUp与CutMix增强以提升泛化能力；通过指数移动平均（EMA）权重和测试时增强（TTA）进一步稳定性能。注意力滚出（Attention Rollout）可视化生成临床可解释的预测热图。模型在测试集上达到99.29%准确率、99.25%宏F1分数，对健康与脑膜瘤类别的召回率均为100%，显著优于所有基于CNN的基线方法。
原文摘要 · Abstract (English)
Brain tumors represent one of the most critical neurological conditions, where early and accurate diagnosis is directly correlated with patient survival rates. Manual interpretation of Magnetic Resonance Imaging (MRI) scans is time-intensive, subject to inter-observer variability, and demands significant specialist expertise. This paper proposes a deep learning framework for automated four-class brain tumor classification distinguishing glioma, meningioma, pituitary tumor, and healthy brain tissue from a dataset of 7,023 MRI scans. The proposed system employs a Vision Transformer (ViT-B/16) pretrained on ImageNet-21k as the backbone, augmented with a clinically motivated preprocessing and training pipeline. Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to enhance local contrast and accentuate tumor boundaries invisible to standard normalization. A two-stage fine-tuning strategy is adopted: the classification head is warmed up with the backbone frozen, followed by full fine-tuning with discriminative learning rates. MixUp and CutMix augmentation is applied per batch to improve generalization. Exponential Moving Average (EMA) of weights and Test-Time Augmentation (TTA) further stabilize and boost performance. Attention Rollout visualization provides clinically interpretable heatmaps of the brain regions driving each prediction. The proposed model achieves a test accuracy of 99.29%, macro F1-score of 99.25%, and perfect recall on both healthy and meningioma classes, outperforming all CNN-based baselines
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