A novel dilated contextual attention module for breast cancer mitosis cell detection.

ABSTRACT: Background and object: Mitotic count (MC) is a critical histological parameter for accurately assessing the degree of invasiveness in breast cancer, holding significant clinical value for cancer treatment and prognosis. However, accurately identifying mitotic cells poses a challenge due to their morphological and size diversity. Objective: We propose a novel end-to-end deep-learning method for identifying mitotic cells in breast cancer pathological images, with the aim of enhancing the performance of recognizing mitotic cells. Methods: We introduced the Dilated Cascading Network (DilCasNet) composed of detection and classification stages. To enhance the model's ability to capture distant feature dependencies in mitotic cells, we devised a novel Dilated Contextual Atten

SUBMITTER: Li Z 

PROVIDER: S-EPMC10850563 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature