One of the major obstacles in reaching diagnostic consensus is observer variability. With the
recent success of artificial intelligence, particularly the deep networks, the question emerges …

Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and
efficient diagnostic methods to guide treatment decisions. Traditional pathology practices …

For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic
algorithms are routinely implemented in clinical practice. Artificial intelligence-based image …

Intratumoral heterogeneity can wreak havoc on current precision medicine strategies
because of challenges in sufficient sampling of geographically separated areas of …

Intratumoral heterogeneity arising from tumor evolution poses significant challenges
biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) …

Recent multi‐omics analyses paved the way for a comprehensive understanding of
pathological processes. However, only few studies have explored Alzheimer's disease (AD) …

Supervised learning tasks such as cancer survival prediction from gigapixel whole slide
images (WSIs) are a critical challenge in computational pathology that requires modeling …

Despite numerous advances in our molecular understanding of cancer biology, success in
precision medicine trials has remained elusive for many malignancies. Emerging evidence …

Context Computational pathology is a new interdisciplinary field that combines traditional
pathology with modern technologies such as digital imaging and machine learning to better …

Although deep learning networks applied to digital images have shown impressive results
for many pathology-related tasks, their black-box approach and limitation in terms of …