Correct classification is the main aspect in evaluating the quality of an artificial intelligence system, but what happens when you reach top accuracy and no method explains how it works? In our study, we aim at addressing the black-box problem using an ad-hoc built classifier for lung ultrasound images.
Explaining ensemble models for lung ultrasound classification
Bruno A;Ignesti G;Martinelli M
2023
Abstract
Correct classification is the main aspect in evaluating the quality of an artificial intelligence system, but what happens when you reach top accuracy and no method explains how it works? In our study, we aim at addressing the black-box problem using an ad-hoc built classifier for lung ultrasound images.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
prod_482760-doc_199263.pdf
accesso aperto
Descrizione: Explaining ensemble models for lung ultrasound classification
Tipologia:
Versione Editoriale (PDF)
Licenza:
Creative commons
Dimensione
579.44 kB
Formato
Adobe PDF
|
579.44 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
