Obtaining models that capture imaging markers relevant for disease progression and
treatment monitoring is challenging. Models are typically based on large amounts of data with …

Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the
problem of explainability is as old as AI itself and classic AI represented comprehensible …

Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative
metrics—the so-called radiomic features—within medical images. Radiomic features …

Understanding how the structure of cognition arises from the topographical organization of
the cortex is a primary goal in neuroscience. Previous work has described local functional …

Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming.
Furthermore, not all possibly relevant markers may be known and sufficiently well …

Purpose Development and validation of a fully automated method to detect and quantify
macular fluid in conventional OCT images. Design Development of a diagnostic modality. …

The capacity to identify the unique functional architecture of an individual's brain is a crucial
step toward personalized medicine and understanding the neural basis of variation in …

Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists, …

Understanding how cognitive functions emerge from brain structure depends on quantifying
how discrete regions are integrated within the broader cortical landscape. Recent work …

Glioblastoma is characterized by widespread genetic and transcriptional heterogeneity, yet
little is known about the role of the epigenome in glioblastoma disease progression. Here, …