融合边缘、细胞与组织特征，提升乳腺癌病理图像分类准确率
FECT: Classification of Breast Cancer Pathological Images Based on Fusion Features
- 通过融合边缘、细胞和组织多尺度特征提升分类性能
- 在BRACS数据集上准确率与F1分数均优于现有方法
- 特征融合方式贴近病理医生诊断逻辑，具备临床可解释性
乳腺癌是全球女性中最常见的癌症之一，早期诊断与精准分类至关重要。随着深度学习与计算机视觉的发展，乳腺组织病理图像的自动分类已成为研究热点。现有方法通常依赖单一细胞或组织特征，且未针对难分类类别设计形态学特征，导致分类效果不佳。为此，本文提出一种新型乳腺癌组织分类模型FECT，融合边缘、细胞与组织特征，采用ResMTUNet与注意力聚合器提取并整合多维特征。在BRACS数据集上的大量测试表明，该模型在分类准确率与F1分数上均优于当前先进方法。由于其特征融合机制符合病理医生的诊断思路，模型具备良好可解释性，有望在未来的临床应用中发挥重要作用。
原文摘要 · Abstract (English)
Breast cancer is one of the most common cancers among women globally, with early diagnosis and precise classification being crucial. With the advancement of deep learning and computer vision, the automatic classification of breast tissue pathological images has emerged as a research focus. Existing methods typically rely on singular cell or tissue features and lack design considerations for morphological characteristics of challenging-to-classify categories, resulting in suboptimal classification performance. To address these problems, we proposes a novel breast cancer tissue classification model that Fused features of Edges, Cells, and Tissues (FECT), employing the ResMTUNet and an attention-based aggregator to extract and aggregate these features. Extensive testing on the BRACS dataset demonstrates that our model surpasses current advanced methods in terms of classification accuracy and F1 scores. Moreover, due to its feature fusion that aligns with the diagnostic approach of pathologists, our model exhibits interpretability and holds promise for significant roles in future clinical applications.
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