Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT.

ABSTRACT: Cervical cell classification has important clinical significance in cervical cancer screening at early stages. However, there are fewer public cervical cancer smear cell datasets, the weights of each classes' samples are unbalanced, the image quality is uneven, and the classification research results based on CNN tend to overfit. To solve the above problems, we propose a cervical cell image generation model based on taming transformers (CCG-taming transformers) to provide high-quality cervical cancer datasets with sufficient samples and balanced weights, we improve the encoder structure by introducing SE-block and MultiRes-block to improve the ability to extract information from cervical cancer cells images; we introduce Layer Normlization to standardize the data, which is convenient for t

SUBMITTER: Zhao C 

PROVIDER: S-EPMC8933771 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature