Exploring the differences between Multi-task and Single-task with the use of hxplainable AI for lung nodule classification

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Fernandes, L (Autor) Outra; Pereira, T (Autor) Outra; Oliveira, HP (Autor) FCUP

Ata de Conferência Internacional

Título: 2024 IEEE 37TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS 2024

Páginas: 418-423

37th International Symposium on Computer-Based Medical Systems (CBMS)

Guadalajara, MEXICO, JUN 26-28, 2024

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-016-DYX

DOI: 10.1109/cbms61543.2024.00075

Abstract (EN): Currently, lung cancer is one of the deadliest diseases that affects millions of people globally. However, Artificial Intelligence is being increasingly integrated with healthcare practices, with the goal to aid in the early diagnosis of lung cancer. Although such methods have shown very promising results, they still lack transparency to the user, which consequently could make their generalised adoption a challenging task. Therefore, in this work we explore the use of post-hoc explainable methods, to better understand the inner-workings of an already established multitasking framework that executes the segmentation and the classification task of lung nodules simultaneously. The idea behind such study is to understand how a multitasking approach impacts the model's performance in the lung nodule classification task when compared to single-task models. Our results show that the multitasking approach works as an attention mechanism by aiding the model to learn more meaningful features. Furthermore, the multitasking framework was able to achieve a better performance in regard to the explainability metric, with an increase of 7% when compared to our baseline, and also during the classification and segmentation task, with an increase of 4.84% and 15.03%; for each task respectively, when also compared to the studied baselines.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6