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Non-Contact Respiratory Rate Estimation in Newborns During Quiet Sleep Using Video Magnification Techniques and a 3D Convolutional Neural Network
(IEEE, 2024)
Escobedo Gordillo, Andrés Emiliano
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Rivas-Scott, Orlando Yael
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Cabon, Sandie
In this paper, we present a new non-contact strategy to estimate the respiratory rate (RR) in a neonatal intensive care unit (NICU) based on the Eulerian motion video magnification technique and a 3D Convolutional Neural Network (3D CNN). The magnification procedure was carried out using the Hermite decomposition. The RR is estimated using a 3D CNN and a region of interest (ROI) detected manually. We have tested the method on 8 infants in NICU during quiet sleep. A contact respiratory signal is acquired synchronously to the videos to compute the RR as reference for training the CNN. To compare the performance of the method, we compute the Mean Absolute Error, the Root Mean Squared Error and metrics from the Bland and Altman analysis to investigate the agreement of the method with respect to the respiratory signal reference. The proposed solution shows an agreement with respect to the reference of 95% and root mean squared error of 2.88. ©The authors ©IEEE.
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Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis
(IEEE, 2024)
Franco-Gaona, Erick
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Avila-Garcia, Maria-Susana
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Cruz-Aceves, Ivan
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Orocio-Garcia, Hiram-Efrain
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Escobedo-Gordillo, Andrés
Coronary stenosis is a disease that claims millions of lives each year. Early detection of this condition is crucial for patient survival. Currently, physicians perform detection by x-ray angiograms, however, the variability of diagnoses and the difficulty of access to expertise has led to the need for automated, computer-assisted diagnosis. In this work explores the use of deep learning to classify stenosis or non-stenosis in angiogram images using convolutional neural networks from scratch. A methodology to fine-tuning network architectures automatically using metaheuristic optimization techniques is proposed, demonstrating superior performance to fine-tuning empirically and proposing a new architecture in the literature to classify coronary stenosis. The proposed architectures achieved 86.02% and 95.67% F1-score with simulated annealing and iterated local search techniques, respectively. ©The authors ©IEEE
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Evaluation of Dataset Distribution in Biomedical Image Classification Against Image Acquisition Distortions
(IEEE, 2024)
Aguilera-González, Santiago
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Renza, Diego
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One of the conditions expected when training a machine learning model is that the inference data should be independently and identically distributed (i.i.d.) with respect to the training data. However, as the real world evolves, this condition can be lost, which is known as shift distribution. This situation can affect the performance of a machine learning model, so the question is how to evaluate (without training a model) the presence of shift distribution. Consequently, this paper presents a proposal to determine the degree of distribution shift in medical image datasets in the face of possible distortions due to the capture system. The methodology is based on Cumulative Spectral Gradient (CSG) metric and it is applied to three biomedical imaging datasets extracted from MedMNIST, an initiative that has compiled several standardized biomedical datasets: PneumoniaMNIST, BreastMNIST and RetinaMNIST. Thanks to this methodology, it is possible to evaluate which types of modifications have a greater impact on the generalization of the models, as well as to determine if there are classes more affected by corruptions. ©The authors ©IEEE.
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