A Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation
Autor:
Amador-Domínguez, Elvira
; Serrano, Emilio
; Manrique, Daniel
; Bajo, Javier
Fecha:
12/2021Palabra clave:
Revista / editorial:
International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)Tipo de Ítem:
articleDirección web:
https://www.ijimai.org/journal/bibcite/reference/3000Resumen:
Case-Based Reasoning models are one of the most used reasoning paradigms in expert-knowledge-driven areas. One of the most prominent fields of use of these systems is the medical sector, where explainable models are required. However, these models are considerably reliant on user input and the introduction of relevant curated data. Deep learning approaches offer an analogous solution, where user input is not required. This paper proposes a hybrid Case-Based Reasoning, Deep Learning framework for medical-related applications, focusing on the generation of medical reports. The proposal combines the explainability and user-focused approach of case-based reasoning models with the deep learning techniques performance. Moreover, the framework is fully modular to fit a wide variety of tasks and data, such as real-time sensor captured data, images, or text, to name a few. An implementation of the proposed framework focusing on radiology report generation assistance is provided. This implementation is used to evaluate the proposal, showing that it can provide meaningful and accurate corrections, even when the amount of information available is minimal. Additional tests on the optimization degree of the case base are also performed, evidencing how the proposed framework can optimize this base to achieve optimal performance.
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(es)
Estadísticas de uso
Año |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
2021 |
2022 |
2023 |
2024 |
Vistas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
93 |
97 |
149 |
Descargas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
59 |
64 |
106 |
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
A Review of Bias and Fairness in Artificial Intelligence
González-Sendino, Rubén; Serrano, Emilio; Bajo, Javier; Novais, Paulo (International Journal of Interactive Multimedia and Artificial Intelligence, 11/2023)Automating decision systems has led to hidden biases in the use of artificial intelligence (AI). Consequently, explaining these decisions and identifying responsibilities has become a challenge. As a result, a new field ... -
COVID Isolation Eating Scale (CIES): Analysis of the impact of confinement in eating disorders and obesity—A collaborative international study
Fernández‐Aranda, Fernando; Munguía, Lucero; Mestre-Bach, Gemma ; Steward, Trevor; Etxandi, Mikel; Baenas, Isabel; Granero, Roser; Sánchez, Isabel; Ortega, Emilio; Andreu, Alba; Moize, Violeta L.; Fernández‐Real, José M.; Tinahones, Francisco J.; Diegüez, Carlos; Frühbeck, Gema; Le Grange, Daniel; Tchanturia, Kate; Karwautz, Andreas; Zeiler, Michael; Favaro, Ángela; Claes, Laurence; Luyckx, Koen; Shekriladze, Ia; Serrano‐Troncoso, Eduardo; Rangil, Teresa; Loran Meler, Maria Eulalia; Soriano‐Pacheco, José; Carceller‐Sindreu, Mar; Bujalance‐Arguijo, Sara; Lozano, Meritxell; Linares, Raquel; Gudiol, Carlota; Carratala, Jordi; Sánchez‐González, Jéssica; Machado, Paulo PP; Håkansson, Anders; Túry, Ferenc; Pászthy, Bea; Stein, Daniel; Papezová, Hana; Bax, Brigita; Borisenkov, Mikhail F.; Popov, Sergey V.; Kim, Youl‐Ri; Nakazato, Michiko; Godart, Nathalie; van Voren, Robert; Ilnytska, Tetiana; Chen, Jue; Rowlands, Katie; Treasure, Janet; Jiménez‐Murcia, Susana (European Eating Disorders Review, 2020)Confinement during the COVID-19 pandemic is expected to have a serious and complex impact on the mental health of patients with an eating disorder (ED) and of patients with obesity. The present manuscript has the following ... -
Impact of COVID-19 Lockdown in Eating Disorders: A Multicentre Collaborative International Study
Baenas, Isabel; Etxandi, Mikel; Munguía, Lucero; Granero, Roser; Mestre-Bach, Gemma ; Sánchez, Isabel; Ortega, Emilio; Andreu, Alba; Moize, Violeta L.; Fernández-Real, Jose-Manuel; Tinahones, Francisco J.; Diéguez, Carlos; Frühbeck, Gema; Le Grange, Daniel; Tchanturia, Kate; Karwautz, Andreas; Zeiler, Michael; Imgart, Hartmut; Zanko, Annika; Favaro, Ángela; Claes, Laurence; Shekriladze, Ia; Serrano‐Troncoso, Eduardo; Cecilia-Costa, Raquel; Rangil, Teresa; Loran Meler, Maria Eulalia; Soriano‐Pacheco, José; Carceller‐Sindreu, Mar; Navarrete, Rosa; Lozano, Meritxell; Linares, Raquel; Gudiol, Carlota; Carratala, Jordi; Plana, María T.; Graell, Montserrat; González-Parra, David; Gómez-del Barrio, José A.; Sepúlveda, Ana R.; Sánchez-González, Jéssica; Machado, Paulo PP; Håkansson, Anders; Túry, Ferenc; Pászthy, Bea; Stein, Daniel; Papezová, Hana; Gricova, Jana; Bax, Brigita; Borisenkov, Mikhail F.; Popov, Sergey V.; Gubin, Denis G.; Petrov, Ivan M.; Isakova, Dilara; Mustafina, Svetlana V.; Kim, Youl‐Ri; Nakazato, Michiko; Godart, Nathalie; van Voren, Robert; Ilnytska, Tetiana; Chen, Jue; Rowlands, Katie; Voderholzer, Ulrich; Monteleone, Alessio M.; Treasure, Janet; Jiménez-Murcia, Susana; Fernández-Aranda, Fernando (Nutrients, 01/2022)Background. The COVID-19 lockdown has had a significant impact on mental health. Patients with eating disorders (ED) have been particularly vulnerable. Aims. (1) To explore changes in eating-related symptoms and general ...