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2025-10-06 更新
Brain Tumor Classification on MRI in Light of Molecular Markers
Authors:Jun Liu, Geng Yuan, Weihao Zeng, Hao Tang, Wenbin Zhang, Xue Lin, XiaoLin Xu, Dong Huang, Yanzhi Wang
In research findings, co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. The ability to predict 1p19q status is critical for treatment planning and patient follow-up. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection. Although public networks such as RestNet and AlexNet can effectively diagnose brain cancers using transfer learning, the model includes quite a few weights that have nothing to do with medical images. As a result, the diagnostic results are unreliable by the transfer learning model. To deal with the problem of trustworthiness, we create the model from the ground up, rather than depending on a pre-trained model. To enable flexibility, we combined convolution stacking with a dropout and full connect operation, it improved performance by reducing overfitting. During model training, we also supplement the given dataset and inject Gaussian noise. We use three–fold cross-validation to train the best selection model. Comparing InceptionV3, VGG16, and MobileNetV2 fine-tuned with pre-trained models, our model produces better results. On an validation set of 125 codeletion vs. 31 not codeletion images, the proposed network achieves 96.37% percent F1-score, 97.46% percent precision, and 96.34% percent recall when classifying 1p/19q codeletion and not codeletion images.
在研究过程中发现，1p/19q基因的联合缺失与低级别胶质瘤的临床结果有关。预测1p19q状态的能力对于治疗计划和患者随访至关重要。本研究旨在利用基于MRI的卷积神经网络进行脑癌检测。尽管RestNet和AlexNet等公共网络可以通过迁移学习有效地诊断脑癌，但模型中包含许多与医学图像无关的权重。因此，迁移学习模型的诊断结果不可靠。为了解决可信度问题，我们从零开始构建模型，而不是依赖于预训练模型。为了提高灵活性，我们将卷积堆叠与丢弃和完全连接操作相结合，这通过减少过拟合提高了性能。在模型训练过程中，我们还补充了给定的数据集并注入了高斯噪声。我们使用三折交叉验证来训练最佳选择模型。与预训练模型微调过的InceptionV3、VGG16和MobileNetV2相比，我们的模型产生了更好的结果。在125张代码缺失与31张非代码缺失图像的验证集上，所提出的网络在分类1p/19q代码缺失和非代码缺失图像时，达到了96.37%的F1分数、97.46%的精确度和96.34%的召回率。
论文及项目相关链接
PDF ICAI’22 - The 24th International Conference on Artificial Intelligence, The 2022 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE’22), Las Vegas, USA. The paper acceptance rate 17% for regular papers. The publication of the CSCE 2022 conference proceedings has been delayed due to the pandemic
Summary
该研究通过利用基于MRI的卷积神经网络对低级别胶质瘤中的1p/19q基因共缺失进行预测，旨在提高诊疗效果。研究采用全新构建的模型而非迁移学习模型，通过卷积堆叠、dropout和全局平均池化操作等技术提高诊断准确性并降低过拟合风险。实验结果显示，该模型在分类1p/19q基因共缺失与非共缺失图像时，达到较高的F1分数、精确度和召回率。
Key Takeaways
- 研究发现，在低级别胶质瘤中，联合删除基因状况与治疗效果息息相关，能够准确预测对治疗效果有重要意义。
- 研究使用基于MRI的卷积神经网络进行脑癌检测，目的是改进诊断结果。
- 虽然公共网络如RestNet和AlexNet可通过迁移学习进行脑癌诊断，但其结果可能因存在无关权重而不可靠。因此该研究未采用预训练模型，而是重新构建模型。
- 为解决可靠性问题，结合了卷积堆叠技术、dropout与全局平均池化操作以增强模型性能并降低过拟合风险。
- 在模型训练过程中，除了使用现有数据集外，还添加了高斯噪声以增强模型的泛化能力。
- 采用三折交叉验证训练最佳模型选择。
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