Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …

We propose a novel framework to perform classification via deep learning in the presence of
noisy annotations. When trained on noisy labels, deep neural networks have been observed …

Robust loss functions are essential for training accurate deep neural networks (DNNs) in the
presence of noisy (incorrect) labels. It has been shown that the commonly used Cross …

Learning from noisy data is a challenging task that significantly degenerates the model
performance. In this paper, we present TCL, a novel twin contrastive learning model to learn …

Modern neural networks have the capacity to overfit noisy labels frequently found in real-
world datasets. Although great progress has been made, existing techniques are very …

Label noise hampers supervised training of neural networks. However, data without label
noise is often infeasible to attain, especially for medical tasks. Attaining high-quality medical …

Abstract Self-supervised Contrastive Learning (CL) has been recently shown to be very
effective in preventing deep networks from overfitting noisy labels. Despite its empirical …

Abstract Weakly-Supervised Semantic Segmentation (WSSS) segments objects without
heavy burden of dense annotation. While as a price, generated pseudo-masks exist obvious …

Deep neural networks are known to be data-driven and label noise can have a marked
impact on model performance. Recent studies have shown great robustness to classic …

Parkinson's disease (PD) is a brain disorder that primarily affects motor function, leading to
slow movement, tremor, and stiffness, as well as postural instability and difficulty with …