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Muñoz-Aseguinolaza U, Fernandez-Iriondo I, Rodríguez-Moreno I, Aginako N, Sierra B. Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach. Heliyon 2023; 9:e21203. [PMID: 37885719 PMCID: PMC10598494 DOI: 10.1016/j.heliyon.2023.e21203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 10/18/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023] Open
Abstract
Recent developments in technology and research have offered a wide variety of new techniques for image and data analysis within the medical field. Medical research helps doctors and researchers acquire not only knowledge about health and new diseases, but also techniques of prevention and treatment. In particular, radiomic analysis is mainly used to extract quantitative data from medical images and to build a model strong enough to diagnose focal diseases. However, finding a model capable to fit all patient situations is not an easy task. In this paper frame prediction models and classification models are reported in order to predict the evolution of a given data series and determine whether an anomaly exists or not. This article also shows how to build and make use of a convolutional neural network-based architecture aiming to accomplish prediction task for medical images, not only using common computer tomography scans, but also 3D volumes.
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Affiliation(s)
- Unai Muñoz-Aseguinolaza
- Department of Computer Science and Artificial Intelligence, University of Basque Country, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Izaro Fernandez-Iriondo
- Department of Computer Science and Artificial Intelligence, University of Basque Country, Donostia-San Sebastián, Gipuzkoa, Spain
- Computational Neuroimaging Lab, Biobizkaia Health Research Institute, Bilbao, Spain
| | - Itsaso Rodríguez-Moreno
- Department of Computer Science and Artificial Intelligence, University of Basque Country, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Naiara Aginako
- Department of Computer Science and Artificial Intelligence, University of Basque Country, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Basilio Sierra
- Department of Computer Science and Artificial Intelligence, University of Basque Country, Donostia-San Sebastián, Gipuzkoa, Spain
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Fernandez-Iriondo I, Jimenez-Marin A, Sierra B, Aginako N, Bonifazi P, Cortes JM. Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis. Front Neurosci 2022; 16:889725. [PMID: 35801180 PMCID: PMC9255673 DOI: 10.3389/fnins.2022.889725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Simultaneous mapping of multiple behavioral domains into brain networks remains a major challenge. Here, we shed some light on this problem by employing a combination of machine learning, structural and functional brain networks at different spatial resolutions (also known as scales), together with performance scores across multiple neurobehavioral domains, including sensation, motor skills, and cognition. Provided by the Human Connectome Project, we make use of three cohorts: 640 participants for model training, 160 subjects for validation, and 200 subjects for model performance testing thus enhancing prediction generalization. Our modeling consists of two main stages, namely dimensionality reduction in brain network features at multiple scales, followed by canonical correlation analysis, which determines an optimal linear combination of connectivity features to predict multiple behavioral performance scores. To assess the differences in the predictive power of each modality, we separately applied three different strategies: structural unimodal, functional unimodal, and multimodal, that is, structural in combination with functional features of the brain network. Our results show that the multimodal association outperforms any of the unimodal analyses. Then, to answer which human brain structures were most involved in predicting multiple behavioral scores, we simulated different synthetic scenarios in which in each case we completely deleted a brain structure or a complete resting state network, and recalculated performance in its absence. In deletions, we found critical structures to affect performance when predicting single behavioral domains, but this occurred in a lesser manner for prediction of multi-domain behavior. Overall, our results confirm that although there are synergistic contributions between brain structure and function that enhance behavioral prediction, brain networks may also be mutually redundant in predicting multidomain behavior, such that even after deletion of a structure, the connectivity of the others can compensate for its lack in predicting behavior.
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Affiliation(s)
- Izaro Fernandez-Iriondo
- Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Doctoral Programme in Informatics Engineering, University of the Basque Country (UPV/EHU), San Sebastian, Spain
- *Correspondence: Izaro Fernandez-Iriondo
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Basilio Sierra
- Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Naiara Aginako
- Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
| | - Jesus M. Cortes
- Computational Neuroimaging Lab, BioCruces-Bizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain
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