Deep learning segmentation architectures for automatic detection of pancreatic ductal adenocarcinoma in EUS-guided fine-needle biopsy samples based on whole-slide imaging
The findings of this study demonstrated the feasibility of utilizing artificial intelligence for assessing PDAC segmentation in whole-slide imaging, supported by promising scores.
Authors
- Lucian Gheorghe Gruionu (ORCID: https://orcid.org/0000-0002-4526-0864)
- Daniel Pirici (ORCID: https://orcid.org/0000-0001-9192-7319)
- Nona Bejinariu (ORCID: https://orcid.org/0000-0001-7531-6075)
- Nicoleta Podină
- Claudia Valentina Georgescu
- Adrian Săftoiu (ORCID: https://orcid.org/0000-0001-7993-8269)
- Bogdan Silviu Ungureanu (ORCID: https://orcid.org/0000-0002-1984-791X)
- Ștefan Udriștoiu (ORCID: https://orcid.org/0000-0002-5560-6435)
- Alina Constantin (ORCID: https://orcid.org/0000-0001-6411-2376)
- Daniela Elena Burtea
Institutions
- Carol Davila University of Medicine and Pharmacy (RO)
- University of Medicine and Pharmacy of Craiova (RO)
- Emergency University (US)
- University of Craiova (RO)
Publication Details
- Journal
- Endoscopic Ultrasound
- Published
- 2024-11-01
- DOI
- https://doi.org/10.1097/eus.0000000000000094
- Citations
- 2
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 0.57