Skin cancer is a major public health problem that could benefit from computer-aided
diagnosis to reduce the burden of this common disease. Skin lesion segmentation from …

Corneal diseases, uncorrected refractive errors, and cataract represent the major causes of
blindness globally. The number of refractive surgeries, either cornea-or lens-based, is also …

Training deep segmentation models for medical images often requires a large amount of
labeled data. To tackle this issue, semi-supervised segmentation has been employed to …

Deep neural networks have played a vital role in developing automated methods for
addressing medical image segmentation. However, their reliance on labeled data impedes …

Explainable artificial intelligence (XAI) focuses on transparent AI models and decisions,
which are easy to understand, analyze, and augment by a nontechnical audience. Fuzzy …

Mandibular segmentation is an important step in gender identification and age estimation,
which aims to segment the mandible from intact and complete panoramic radiograph. One of …

Ground penetrating radar (GPR) has been commonly applied for the non-destructive
investigation of underground anomalies. This study proposes a robust anomaly detection …

Deep convolutional neural networks have shown eminent performance in medical image
segmentation in supervised learning. However, this success is predicated on the availability …

Semi-supervised learning methods aim to address the scarcity of pixel-level annotations in
medical image segmentation. Previous approaches typically rely on filtering strategies to …

The available medical images with accurate segmentation masks are usually limited due to
the expensive and time-consuming annotation cost. Many semi-supervised approaches tried …