FECT: Classification of Breast Cancer Pathological Images Based on Fusion Features
Journal:
arXiv
Published Date:
Jan 17, 2025
Abstract
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.
