Improving Circulating Tumor Cell Detection Using Image Synthesis and Transformer Models in Cancer Diagnostics
Shuang Liang, Xue Bai, Yu Gu · 2024
Read Improving Circulating Tumor Cell Detection Using Image Synthesis and Transformer Models in Cancer Diagnostics by Shuang Liang, Xue Bai, Yu Gu (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11644996.txt.
Description
Shuang Liang, Xue Bai, and Yu Gu present a 2024 method to find tumor cells that drift in the blood, as a route to early cancer spotting. Such cells are rare, and they vary in size and shape, so machines often miss them. Early spotting matters, since late stage cancer has few good treatments and high death rates.
The team made extra training images with a segment tool and a copy paste trick. They then built a detection network on a transformer design, with parts that adjust for scale and shape. A revised training loss helped the model learn from both made and real images without drifting. On five measures, the model scored very high, with strong accuracy and recall. The authors offer it as a diagnostic aid.
Catalog record
| CREATOR | Shuang Liang, Xue Bai, Yu Gu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |