With the remarkable success of representation learning for prediction problems, we have
witnessed a rapid expansion of the use of machine learning and deep learning for the …

The rapid development of diagnostic technologies in healthcare is leading to higher
requirements for physicians to handle and integrate the heterogeneous, yet complementary …

The analysis of histopathology images with artificial intelligence aims to enable clinical
decision support systems and precision medicine. The success of such applications …

Computational pathology is revolutionizing the field of pathology by integrating advanced
computer vision and machine learning technologies into diagnostic workflows. It offers …

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens
highly depend on the phenotype and topological distribution of constituting histological …

An unresolved issue in contemporary biomedicine is the overwhelming number and
diversity of complex images that require annotation, analysis and interpretation. Recent …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-
supervised models have already achieved outstanding performance with dense pixel-level …

Instance segmentation is the state-of-the-art in object detection, and there are numerous
applications in remote sensing data where these algorithms can produce significant results …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …