Improving a Deep Learning Model to Accurately Diagnose LVNC
Jaime Rafael Barón, Gregorio Bernabé, Pilar González-Férez, José Manuel García, Guillem Casas, Josefa González-Carrillo, Federico Guerra · 2023
Read Improving a Deep Learning Model to Accurately Diagnose LVNC by Jaime Rafael Barón, Gregorio Bernabé, Pilar González-Férez, José Manuel García, Guillem Casas, Josefa González-Carrillo, Federico Guerra (2023) 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-10743747.txt.
Description
Jaime Rafael Barón and co-authors work on finding a rare heart disease with computer help in this 2023 paper. In the disease, the main pumping chamber of the heart has a spongy, loose wall. Spotting it matters for care, but scans are hard to read, so the team aimed to improve how software outlines the chamber.
They used heart scans from three hospitals. Using larger images helped, as did a step that removes false marks made by the network. A newer network design gave the best outlines and the best disease calls, with accuracy near nine in ten. Results differed between hospitals, which shows that making the tool work everywhere is still a problem.
Catalog record
| CREATOR | Jaime Rafael Barón, Gregorio Bernabé, Pilar González-Férez, José Manuel García, Guillem Casas, Josefa González-Carrillo, Federico Guerra |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |