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

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Unsupervised learning has been a long-standing goal of machine learning and is especially
important for medical image analysis, where the learning can compensate for the scarcity of …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a
progressive decline in cognitive abilities. Since AD starts several years before the onset of …

A generative adversarial network (GAN) is one of the most significant research directions in
the field of artificial intelligence, and its superior data generation capability has garnered …

Abstract Machine learning (ML) methods have demonstrated impressive performance in
many application fields such as autopilot, facial recognition, and spam detection …

Bone marrow (BM) examination is an essential step in both diagnosing and managing
numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of …

Technical advancements significantly improve earlier diagnosis of cervical cancer, but
accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence …

Background and objective Cervical cell classification has important clinical significance in
cervical cancer screening at early stages. In contrast with the conventional classification …