In this survey, we present a review of methods and resources for texture recognition,
presenting the most common techniques that have been used in the recent decades, along …

Healthcare agents, in particular in the oncology field, are currently collecting vast amounts of
diverse patient data. In this context, some decision-support systems, mostly based on deep …

Importance The recent advances in the field of machine learning have raised expectations
that computer-aided diagnosis will become the standard for the diagnosis of melanoma …

Logic-based machine learning has the crucial advantage of transparency. However, despite
significant recent progress, further research is needed to close the accuracy gap between …

Histopathological images are used to characterize complex phenotypes such as tumor
stage. Our goal is to associate features of stained tissue images with high-dimensional …

A shared goal of several machine learning communities like continual learning, meta-
learning and transfer learning, is to design algorithms and models that efficiently and …

Early detection of skin cancers like melanoma is crucial to ensure high chances of survival
for patients. Clinical application of Deep Learning (DL)-based Decision Support Systems …

A shared goal of several machine learning communities like continual learning, meta-
learning and transfer learning, is to design algorithms and models that efficiently and …

A deep neural network architecture is proposed in this paper for underwater scene semantic
segmentation. The architecture consists of encoder and decoder networks. Pretrained VGG …

Despite Convolutional Neural Networks having reached human-level performance in some
medical tasks, their clinical use has been hindered by their lack of interpretability. Two major …