This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …

The creation of an image from another and from different types of data including text, scene
graph, and object layout, is one of the very challenging tasks in computer vision. In addition …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Blood cell count is highly useful in identifying the occurrence of a particular disease or
ailment. To successfully measure the blood cell count, sophisticated equipment that makes …

A generative adversarial network (GAN) is one of the most significant research directions in
the field of artificial intelligence, and its superior data generation capability has garnered …

Despite the growing availability of high-quality public datasets, the lack of training samples
is still one of the main challenges of deep-learning for skin lesion analysis. Generative …

Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can
alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a …

Accurate Computer-Assisted Diagnosis, relying on large-scale annotated pathological
images, can alleviate the risk of overlooking the diagnosis. Unfortunately, in medical …

Machine learning can be used to make sense of healthcare data. Probabilistic machine
learning models help provide a complete picture of observed data in healthcare. In this …

Separating and labeling each nuclear instance (instance-aware segmentation) is the key
challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been …