State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.
- DOI
- 10.1016/j.artmed.2026.103481
- Published
- 2026 Oct
- Container
- Artificial intelligence in medicine
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.artmed.2026.103481,
title = {State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.},
author = {Bhat SA and Chen MC and Huang NF},
year = {2026},
journal = {Artificial intelligence in medicine},
doi = {10.1016/j.artmed.2026.103481},
url = {https://doi.org/10.1016/j.artmed.2026.103481}
}RIS
TY - JOUR TI - State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions. AU - Bhat SA AU - Chen MC AU - Huang NF PY - 2026 JO - Artificial intelligence in medicine DO - 10.1016/j.artmed.2026.103481 UR - https://doi.org/10.1016/j.artmed.2026.103481 ER -
APA
SA, B., MC, C., & NF, H. (2026). State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.. Artificial intelligence in medicine. https://doi.org/10.1016/j.artmed.2026.103481
Source records
- pubmed · retrieved 2026-09-25T22:11:13.347Z