Data augmentation involves artificially expanding a dataset by applying various
transformations to the existing data. Recent developments in deep learning have advanced …

Regional dropout strategies have demonstrated to be very effective in improving both the
performance and the generalization capability of deep learning models. However, when …

Cutmix-based data augmentation, which uses a cut-and-paste strategy, has shown
remarkable generalization capabilities in deep learning. However, existing methods …

Due to the high cost of annotation or the rarity of some diseases, medical image
segmentation is often limited by data scarcity and the resulting overfitting problem. Self …

Medical image segmentation often faces challenges related to overfitting, primarily due to
the limited and complex training samples. This challenge often prompts the use of self …

Data augmentation is an important paradigm for boosting the generalization capability of
deep learning in image classification tasks. Image augmentation using cut-and-paste …

To create robust and adaptable methods for lung pneumonia diagnosis and the assessment
of its severity using chest X-rays (CXR), access to well-curated, extensive datasets is crucial …

To develop generic and reliable approaches for diagnosing and assessing the severity of
COVID-19 from chest X-rays (CXR), a large number of well-maintained COVID-19 datasets …

Data augmentation can mitigate overfitting problems in data exploration without increasing
the size of the model. Existing cutmix-based data augmentation has been proven to …

The detection of various subtypes of Acute Lymphoblastic Leukemia (ALL) is crucial for
precise medical identification, even though it is often hindered by the diverse appearance of …