Accurate Identification of Cancer Cells in Complex Pre‐Clinical Models Using a Deep‐Learning Neural Network: A Transfection‐Free Approach (Adv. Biology 11/2024)
Accurate Identification of Cancer Cells Distinguishing the contribution of different cell types in co-cultures is a major challenge. Marilisa Cortesi, Caroline E. Ford, and co-workers have addressed it through a deep learning-based software tool that distinguishes healthy and cancer cells solely from the shape of the nucleus. This method opens to the possibility of using a wide variety of cell types, including patient-derived ones, in co-cultures. More details can be found in article number 2400034. Image created by Dr. Tim Salita.
Authors
- Ellen Barlow (ORCID: https://orcid.org/0000-0001-7864-1252)
- Caroline E. Ford (ORCID: https://orcid.org/0000-0002-4438-2309)
- Kristina Warton (ORCID: https://orcid.org/0000-0002-9129-9081)
- Marilisa Cortesi (ORCID: https://orcid.org/0000-0002-3731-7760)
- Dongli Liu (ORCID: https://orcid.org/0000-0002-1755-8516)
- Elyse Powell
Institutions
- UNSW Sydney (AU)
- University of Bologna (IT)
Publication Details
- Journal
- Advanced Biology
- Published
- 2024-11-01
- DOI
- https://doi.org/10.1002/adbi.202470112
- Citations
- 1
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.66