Evolutionary design of explainable algorithms for biomedical image segmentation.

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting "black box" models are difficult to interpret.

Mitotic arrest affects clustering of tumor cells.

Background: Cancer cell aggregation is a key process involved in the formation of tumor cell clusters. It has recently been shown that clusters of circulating tumor cells (CTCs) have an increased metastatic potential compared to isolated circulating tumor cells. Several widely used chemotherapeutic agents that target the cytoskeleton microtubules and cause cell cycle arrest at mitosis have been reported to modulate CTC number or the size of CTC clusters.