Multiple Sclerosis (MS) lesions' segmentation is difficult due to their variegated sizes,
shapes, and intensity levels. Besides this, the class imbalance problem and the availability …

Classifying and segmenting skin cancer represent pivotal objectives for automated
diagnostic systems that utilize dermoscopy images. However, these tasks present significant …

In recent years, contrastive learning has been a highly favored method for self-supervised
representation learning, which significantly improves the unsupervised training of deep …

In 3D medical image segmentation, Semi-Supervised Learning (SSL) methods have shown
strong potential with limited labeled data. However, most existing SSL models fail to …

Protecting power infrastructure through visual surveillance can assure the safe operation of
a power system, especially in unstructured environments where leaning utility poles are …

Medical image analysis faces a significant challenge in the scarcity of annotated data, which
is crucial for develo** generalizable Deep Learning (DL) models that require extensive …

Blind image inpainting, the task of detecting corrupted regions with diverse patterns within
an image and then generating plausible content for the corrupted regions, remains a both …

In the biomedical imaging domain, large preprocessed samples of training annotated
images are required in techniques employing neural networks for effective training, which …

As technology advances, solving image segmentation challenges in complex backgrounds
has become a key issue across various fields. Traditional image segmentation methods …