MacNet: a mobile attention classification network combining convolutional neural network and transformer for the differentiation of cervical cancer
We have proposed a lightweight neural network method that innovatively combines attention mechanisms with convolutional neural networks (CNNs) to efficiently utilize multiscale information from histopathological images. This integration enables the precise quantitative display of different stages of differentiation in cervical cancer. By doing so, our method not only enhances diagnostic accuracy but also provides clinicians with a more effective tool for faster and more reliable diagnosis, representing a significant advancement in the field of pathological imaging.
Authors
- Zhenxing Huang (ORCID: https://orcid.org/0000-0001-5012-1992)
- Dong Liang (ORCID: https://orcid.org/0000-0003-2784-3449)
- Zhanli Hu (ORCID: https://orcid.org/0000-0003-0618-6240)
- Yuanyuan Lei (ORCID: https://orcid.org/0000-0002-9805-591X)
- Meiyong Huang
- Yu Liu (ORCID: https://orcid.org/0000-0002-8417-6913)
- Zhou Liu (ORCID: https://orcid.org/0009-0008-1471-4369)
- Wenting Huang
- Wenbo Li (ORCID: https://orcid.org/0009-0004-8574-7046)
- Yi An (ORCID: https://orcid.org/0000-0001-5024-7624)
Institutions
- Chinese Academy of Sciences (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-31
- DOI
- https://doi.org/10.21037/qims-24-810
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- National Natural Science Foundation of China