An automatic detection model for spread through air spaces in postoperative pathological sections based on deep learning in NSCLC
< .05). We propose a deep learning-based artificial intelligence framework for automated STAS detection and quantification in digital whole-slide images of NSCLC. This model holds promising potential to assist pathologists in achieving more comprehensive and accurate diagnosis of STAS.
Authors
- Hongxue Meng (ORCID: https://orcid.org/0000-0002-1831-2737)
- Yu Zhao (ORCID: https://orcid.org/0000-0003-1874-908X)
- Shuzhe Deng (ORCID: https://orcid.org/0000-0002-8687-976X)
- Fenqi Du (ORCID: https://orcid.org/0000-0003-4278-8791)
- E.S. LIN
- Yuxin Kang (ORCID: https://orcid.org/0009-0007-2065-465X)
- Mingyuan Guo (ORCID: https://orcid.org/0009-0009-0140-5425)
- Yu Zhang
Institutions
- Beihua University (CN)
- Harbin Medical University (CN)
- Northwest University (US)
- Third Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Computer Assisted Surgery
- Published
- 2026-05-22
- DOI
- https://doi.org/10.1080/24699322.2026.2674321
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Scientific Research Foundation of Graduate School of Harbin Medical University: Sino Russian Special Fund