- author = "Leonardo Lucio Custode and Federico Mento and Francesco Tursi and Andrea Smargiassi and Riccardo Inchingolo and Tiziano Perrone and Libertario Demi and Giovanni Iacca",
- title = "Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees",
- journal = "Applied Soft Computing",
- year = "2023",
- volume = "133",
- pages = "109926",
- month = jan,
- keywords = "genetic algorithms, genetic programming, grammatical evolution, DEAP, AI, ANN, COVID-19, Lung ultrasound, Decision trees, Evolutionary algorithms, Neuro-symbolic artificial intelligence",
- ISSN = "1568-4946",
-
URL = "
https://human-competitive.org/sites/default/files/custodeentryform.txt",
-
URL = "
https://human-competitive.org/sites/default/files/custodepaperb.pdf",
-
URL = "https://www.sciencedirect.com/science/article/pii/S1568494622009759",
-
DOI = "
10.1016/j.asoc.2022.109926",
-
code_url = "https://gitlab.com/leocus/neurosymbolic-covid19-scoring",
- size = "34 pages",
- abstract = "COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyse the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.",
-
notes = "Entered 2023 HUMIES
Also known as \cite{CUSTODE2023109926}",
