Detecting malignant pulmonary nodules at an early stage can allow medical interventions
which may increase the survival rate of lung cancer patients. Using computer vision …

This paper presents an effective and general data augmentation framework for medical
image segmentation. We adopt a computationally efficient and data-efficient gradient-based …

Detecting crops accurately is the key challenge for harvesting robots, and deep learning
methods are commonly used for this purpose. However, these methods require a large …

Cyberattacks from within an organization's trusted entities are known as insider threats.
Anomaly detection using deep learning requires comprehensive data, but insider threat data …

The limited quantity of training data can hamper supervised machine learning methods, that
generally need large amounts of data to avoid overfitting. Data augmentation has a long …

Deep learning models have been successful in computer vision and medical image
analysis. However, training these models frequently requires large labeled image sets …

Conclusion➢ Overfitting under class imbalance leads to loss of sensitivity.➢ The distribution
of logit activations when processing unseen test samples of an under-represented class …