In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Understanding intratumoral heterogeneity—the molecular variation among cells within a
tumor—promises to address outstanding questions in cancer biology and improve the …

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic
potential and poor prognosis, and has limited treatment options. The current standard of …

Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised
classification in whole slide image (WSI) based pathology diagnosis. However, the current …

Simple Summary Diagnosing cancer at an early stage increases the chance of performing
effective treatment in many tumour groups. Key approaches include screening patients who …

Real‐time monitoring of wound healing remains a major challenge in clinical tissue
regeneration, calling the need for the development of biomaterial‐guided on‐site monitoring …

The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death.
Determining a patient's optimal therapy is a challenge, where oncologists must select a …

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been
applied to many areas in different domains such as health care and drug design. Cancer …