Abstract The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion
analysis is an emerging field of research that has the potential to alleviate the burden and …

Skin disorders significantly impact quality of life, necessitating advanced diagnostic tools.
Machine Learning (ML) and Deep Learning (DL), particularly Convolutional Neural …

Accurate segmentation of skin lesions within dermoscopic images plays a crucial role in the
timely identification of skin cancer for computer-aided diagnosis on mobile platforms …

Purpose To develop a deep learning model to accurately detect anterior cruciate ligament
(ACL) ruptures on magnetic resonance imaging (MRI) and to evaluate its effect on the …

Segment anything models (SAMs) are gaining attention for their zero-shot generalization
capability in segmenting objects of unseen classes and in unseen domains when properly …

In recent years, deep learning (DL) has shown great potential in the field of dermatological
image analysis. However, existing datasets in this domain have significant limitations …

Dermoscopy is a noninvasive way to examine and diagnose skin lesions, eg nevus and
melanoma, and is a critical step for skin cancer detection. Accurate classification of …

Medical image segmentation models often fail to generalize well when applied to new
datasets, hindering their usage in clinical practice. Existing random-convolution-based …

Shortcut learning is a phenomenon where machine learning models prioritize learning
simple, potentially misleading cues from data that do not generalize well beyond the training …

Medical image segmentation is crucial in accurately identifying and delineating regions of
interest in medical images, which can inform the diagnosis and treatment of various …