This review provides the feasible literature on drug discovery through ML tools and
techniques that are enforced in every phase of drug development to accelerate the research …

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

Computational pathology, has witnessed considerable progress in the development of both
task-specific predictive models and task-agnostic self-supervised vision encoders …

Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …

Traditional pathology approaches have played an integral role in the delivery of diagnosis,
semi-quantitative or qualitative assessment of protein expression, and classification of …

Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Radiomic analysis offers a powerful tool for the extraction of clinically relevant information
from radiologic imaging. Radiomics can be used to predict patient outcome through …

The introduction of machine learning (ML) in organizations comes with the claim that
algorithms will produce insights superior to those of experts by discovering the" truth" from …

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 …

Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with
potential applications being demonstrated across various domains of medicine. However …