FECT: Classification of Breast Cancer Pathological Images Based on Fusion Features
This work addresses early diagnosis and precise classification of breast cancer for women, representing an incremental improvement over existing methods.
The paper tackled the problem of suboptimal classification performance in breast cancer pathological images by proposing a novel model that fuses edge, cell, and tissue features, achieving improved accuracy and F1 scores on the BRACS dataset.
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.