对比三种3D UNet模型，用XAI提升脑肿瘤分割可解释性。
Demystifying Deep Learning-based Brain Tumor Segmentation with 3D UNets and Explainable AI (XAI): A Comparative Analysis
- 用ResUNet、AttUNet与传统UNet对比，评估分割性能。
- ResUNet在Dice、Jaccard等指标上最优，准确率更高。
- 通过Grad-CAM和注意力可视化增强医生对模型的信任。
本研究探讨了可解释人工智能（XAI）在提升MRI图像中脑肿瘤分割精度方面的应用，旨在辅助临床决策。聚焦于使用UNet模型进行分割，并结合梯度加权类激活映射（Grad-CAM）与基于注意力的可视化技术以增强模型可解释性。评估了三种深度学习模型：UNet、残差UNet（ResUNet）和注意力UNet（AttUNet）。在BraTS2020公开数据集上比较三者表现，使用Adam优化器，在最新硬件上训练并验证，评估指标包括训练/验证/推理时间、分割相似性系数与损失函数、分类性能。最终测试中，ResUNet在Dice、Jaccard相似度及准确率、召回率、F1分数上均领先。Grad-CAM揭示各模型关注的肿瘤子区域，注意力可视化解析了AttUNet模块工作机制。结果表明ResUNet为最优模型，建议用于未来临床自动化分割。代码与权重已开源。
原文摘要 · Abstract (English)
The current study investigated the use of Explainable Artificial Intelligence (XAI) to improve the accuracy of brain tumor segmentation in MRI images, with the goal of assisting physicians in clinical decision-making. The study focused on applying UNet models for brain tumor segmentation and using the XAI techniques of Gradient-weighted Class Activation Mapping (Grad-CAM) and attention-based visualization to enhance the understanding of these models. Three deep learning models - UNet, Residual UNet (ResUNet), and Attention UNet (AttUNet) - were evaluated to identify the best-performing model. XAI was employed with the aims of clarifying model decisions and increasing physicians' trust in these models. We compared the performance of two UNet variants (ResUNet and AttUNet) with the conventional UNet in segmenting brain tumors from the BraTS2020 public dataset and analyzed model predictions with Grad-CAM and attention-based visualization. Using the latest computer hardware, we trained and validated each model using the Adam optimizer and assessed their performance with respect to: (i) training, validation, and inference times, (ii) segmentation similarity coefficients and loss functions, and (iii) classification performance. Notably, during the final testing phase, ResUNet outperformed the other models with respect to Dice and Jaccard similarity scores, as well as accuracy, recall, and F1 scores. Grad-CAM provided visuospatial insights into the tumor subregions each UNet model focused on while attention-based visualization provided valuable insights into the working mechanisms of AttUNet's attention modules. These results demonstrated ResUNet as the best-performing model and we conclude by recommending its use for automated brain tumor segmentation in future clinical assessments. Our source code and checkpoint are available at https://github.com/ethanong98/MultiModel-XAI-Brats2020
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。