Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …

Deep-learning methods for computational pathology require either manual annotation of
gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and …

Abstract The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …

The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …

Computational methods have made substantial progress in improving the accuracy and
throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still …

Background Gastrointestinal cancers account for approximately 20% of all cancer diagnoses
and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence–based …

During tumor progression, cancer cells come into contact with various non-tumor cell types,
but it is unclear how tumors adapt to these new environments. Here, we integrate spatially …

Cancer immunotherapies have rapidly changed the therapeutic landscape for cancer.
Nevertheless, most of the patients show innate or acquired resistance to these therapies …