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Item type:Publication, Identifying and Mitigating Label Noise in Deep Learning for Image ClassificationLabeling errors in datasets are a persistent challenge in machine learning because they introduce noise and bias and reduce the model’s generalization. This study proposes a novel methodology for detecting and correcting mislabeled samples in image datasets by using the Cumulative Spectral Gradient (CSG) metric to assess the intrinsic complexity of the data. This methodology is applied to the noisy CIFAR-10/100 and CIFAR-10n/100n datasets, where mislabeled samples in CIFAR-10n/100n are identified and relabeled using CIFAR-10/100 as a reference. The DenseNet and Xception models pre-trained on ImageNet are fine-tuned to evaluate the impact of label correction on the model performance. Evaluation metrics based on the confusion matrix are used to compare the model performance on the original and noisy datasets and on the label-corrected datasets. The results show that correcting the mislabeled samples significantly improves the accuracy and robustness of the model, highlighting the importance of dataset quality in machine learning. ©The authors ©MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of Dataset Distribution in Biomedical Image Classification Against Image Acquisition DistortionsOne 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.6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adversarial Validation in Image Classification Datasets by Means of Cumulative Spectral GradientThe main objective of a machine learning (ML) system is to obtain a trained model from input data in such a way that it allows predictions to be made on new i.i.d. (Independently and Identically Distributed) data with the lowest possible error. However, how can we assess whether the training and test data have a similar distribution? To answer this question, this paper presents a proposal to determine the degree of distribution shift of two datasets. To this end, a metric for evaluating complexity in datasets is used, which can be applied in multi-class problems, comparing each pair of classes of the two sets. The proposed methodology has been applied to three well-known datasets: MNIST, CIFAR-10 and CIFAR-100, together with corrupted versions of these. Through this methodology, it is possible to evaluate which types of modification have a greater impact on the generalization of the models without the need to train multiple models multiple times, also allowing us to determine which classes are more affected by corruption. ©The authors ©MDPI ©Algorithms.6