Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …

During the past decades, many automated image analysis methods have been developed
for colonoscopy. Real-time implementation of the most promising methods during …

Digital histopathology poses several challenges such as label noise, class imbalance,
limited availability of labelled data, and several latent biases to deep learning, negatively …

Active learning methods recommend the most informative images from a large unlabeled
dataset for manual labeling. These methods improve the performance of an image classifier …

Echocardiography is a crucial and widely adopted imaging modality for diagnosing and
monitoring cardiovascular diseases. Deep learning has been proven effective in analyzing …

Precursor microRNA (pre-miRNA) sequences are the precursors of microRNAs (miRNAs),
which are non-coding RNA sequences regulating gene expression in organisms …

Convolutional Neural Networks (CNN) are used widely adopted for tasks involved with
Computer Vision, Medical Imaging and Natural language processing. Creating a CNN …

Over the past few years, ML has demonstrated human expert level performance in many
medical image analysis tasks. However, due to the black-box nature of classic deep ML …