融合多模态肺部影像，用深度集成提升癌症分类准确率
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer
- 用PCA与自编码器融合CT和PET图像
- 在三个公开数据集上准确率超现有方法
- 适合医疗影像分析中样本少的场景
本研究针对多模态肺部影像中的癌变与健康切片分类问题。数据包含计算机断层扫描（CT）和正电子发射断层扫描（PET）图像。通过主成分分析（PCA）和自编码器实现PET与CT图像融合，并构建基于深度集成的多模态融合分类器（DEMF），采用多数投票机制进行分类。利用梯度加权类激活映射（Grad-CAM）可视化癌变区域的分类准确性。由于样本量有限，在训练阶段采用随机图像增强策略。DEMF网络有效缓解了医学图像分析中数据稀缺的挑战。在三个公开数据集上与当前先进模型对比，该网络在准确率、F1分数、精确率和召回率等指标上均表现更优，验证了其有效性。
原文摘要 · Abstract (English)
This study focuses on the classification of cancerous and healthy slices from multimodal lung images. The data used in the research comprises Computed Tomography (CT) and Positron Emission Tomography (PET) images. The proposed strategy achieves the fusion of PET and CT images by utilizing Principal Component Analysis (PCA) and an Autoencoder. Subsequently, a new ensemble-based classifier developed, Deep Ensembled Multimodal Fusion (DEMF), employing majority voting to classify the sample images under examination. Gradient-weighted Class Activation Mapping (Grad-CAM) employed to visualize the classification accuracy of cancer-affected images. Given the limited sample size, a random image augmentation strategy employed during the training phase. The DEMF network helps mitigate the challenges of scarce data in computer-aided medical image analysis. The proposed network compared with state-of-the-art networks across three publicly available datasets. The network outperforms others based on the metrics - Accuracy, F1-Score, Precision, and Recall. The investigation results highlight the effectiveness of the proposed network.
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