用自监督学习提升脑瘤分类准确率，仅需少量标注数据即可达到99.6%以上性能。
TumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification
- 基于ResNet-50和四种自监督框架，在4448张MRI上训练模型。
- SimCLR在17类脑瘤上实现99.64%准确率与各项指标均衡表现。
- 结合Grad-CAM等技术，实现诊断决策的可解释性，适合医疗AI研发者。
利用磁共振成像（MRI）进行脑瘤分类对早期诊断和治疗至关重要；然而，肿瘤异质性和标注数据匮乏限制了监督深度学习的应用。本文采用自监督学习（SSL）方法研究多类脑瘤分类问题。以ResNet-50为骨干网络，在包含4,448张MRI、17种肿瘤类型的公开数据集上，评估了SimCLR、BYOL、DINO和Moco v3四种SSL框架。结果表明，SimCLR在该数据集上达到99.64%的准确率、精确率、召回率和F1分数。整个流程包括预处理、微调、线性评估及带数据增强的SSL预训练。当标签有限时，SSL预训练模型在F1分数、召回率、准确率和精确率上均优于监督基线。此外，通过引入梯度加权类激活映射（Grad-CAM）、Grad-CAM++和EigenCAM等可解释AI技术，显著提升了模型决策的可视化能力。这些结果证明了自监督学习在无标注医学数据上的可扩展性与可靠性。
原文摘要 · Abstract (English)
Classifying brain tumors using magnetic resonance imaging (MRI) is crucial for early diagnosis and treatment; however, tumor heterogeneity and a dearth of annotated datasets restrict the use of supervised deep learning approaches. In this work, we use self-supervised learning (SSL) to study multi-class brain tumor classification. Using a ResNet-50 backbone, we evaluate four SSL frameworks including SimCLR, BYOL, DINO, and Moco v3 on a publicly available dataset of 4,448 MRIs with 17 distinct tumor types. On the dataset, SimCLR achieved 99.64% accuracy, 99.64% precision, 99.64% recall, and 99.64% F1-score. The workflow includes preprocessing, fine-tuning, linear evaluation, and SSL pretraining with data augmentations. Results show that, when labels are limited, SSL-pretrained models outperform supervised baselines in terms of F1-score, recall, accuracy, and precision. Additionally, by providing visual insights into model decisions, Explainable AI techniques (Grad-CAM, Grad-CAM++, EigenCAM) enhance interpretability. These results demonstrate SSL's scalability and dependability in diagnosing brain tumors from unlabeled medical data.
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