This Perspective explores the application of machine learning toward improved diagnosis
and treatment. We outline a vision for how machine learning can transform three broad …

The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Microsatellite instability determines whether patients with gastrointestinal cancer respond
exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested …

Nowadays, automatic disease detection has become a crucial issue in medical science due
to rapid population growth. An automatic disease detection framework assists doctors in the …

We use deep transfer learning to quantify histopathological patterns across 17,355
hematoxylin and eosin-stained histopathology slide images from 28 cancer types and …

The advancements of information technology and related processing techniques have
created a fertile base for progress in many scientific fields and industries. In the fields of drug …

Background & Aims The benefit of colonoscopy for colorectal cancer prevention depends on
the adenoma detection rate (ADR). The ADR should reflect the adenoma prevalence rate …

Cancer is the second leading cause of death after cardiovascular diseases. Out of all types
of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types …

Deep learning methods for digital pathology analysis are an effective way to address
multiple clinical questions, from diagnosis to prediction of treatment outcomes. These …

Background and Aims Quality control can decrease variations in the performance of
colonoscopists and improve the effectiveness of colonoscopy to prevent colorectal cancers …