Multi-classification of breast cancer pathology images based on a two-stage hybrid network.

ABSTRACT:

Background and objective

In current clinical medicine, pathological image diagnosis is the gold standard for cancer diagnosis. After pathologists determine whether breast lesions are malignant or benign, further sub-type classification is often necessary.

Methods

For this task, this study designed a multi-classification model for breast cancer pathological images based on a two-stage hybrid network. Due to limited sample size for breast sub-type data, this study selected the ResNet34 network as the base network and improved it as the first-level convolutional network, using transfer learning to assist network training. In order to compensate for the lack of long-distance dependencies in the convolutional network, the second-level network was designed to use Long Short-Term M

SUBMITTER: Wang G 

PROVIDER: S-EPMC11570553 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature