Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Supervised learning techniques construct predictive models by learning from a large
number of training examples, where each training example has a label indicating its ground …

Automated Screening of COVID-19 from chest CT is of emergency and importance during
the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 …

Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …

In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …

Multiple-instance learning (MIL) is a recent machine-learning paradigm that is particularly
well suited to medical image and video analysis (MIVA) tasks. Based solely on class labels …

Labeling a histopathology image as having cancerous regions or not is a critical task in
cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster …

Previous studies on multi-instance learning typically treated instances in the bags as
independently and identically distributed. The instances in a bag, however, are rarely …

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where
an example is described by multiple instances and associated with multiple class labels …

Cross validation allows models to be tested using the full training set by means of repeated
resampling; thus, maximizing the total number of points used for testing and potentially …