Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …

Advances in artificial intelligence have cultivated a strong interest in develo** and
validating the clinical utilities of computer-aided diagnostic models. Machine learning for …

Biomedical image analysis algorithm validation depends on high-quality annotation of
reference datasets, for which labelling instructions are key. Despite their importance, their …

Collecting data from many sources is an essential approach to generate large data sets
required for the training of machine learning models. Trustworthy machine learning requires …

Machine learning (ML) has been adopted in many safety-critical applications like automated
driving and medical diagnosis. Incorrect decisions by ML models can lead to catastrophic …

Medical imaging is an important research field with many opportunities for improving
patients' health. However, there are a number of challenges that are slowing down the …

Federated learning (FL) has received high interest from researchers and practitioners to
train machine learning (ML) models for healthcare. Ensuring the trustworthiness of these …

The COVID-19 pandemic has presented a unique challenge for physicians worldwide, as
they grapple with limited data and uncertainty in diagnosing and predicting disease …

Mislabeled data in large datasets can quickly degrade the performance of machine learning
models. There is a substantial base of work on how to identify and correct instances in data …

Using artificial intelligence (AI) systems for informing high-stakes decisions has become
increasingly pervasive in a variety of domains, including but not limited to hiring, lending, or …