Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

In mammalian brains, millions to billions of cells form complex interaction networks to enable
a wide range of functions. The enormous diversity and intricate organization of cells have …

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a
complete understanding of their intricate organization requires the nanometre-level, three …

The neural circuits responsible for animal behavior remain largely unknown. We summarize
new methods and present the circuitry of a large fraction of the brain of the fruit fly …

A key requirement for the success of supervised deep learning is a large labeled dataset-a
condition that is difficult to meet in medical image analysis. Self-supervised learning (SSL) …

Image registration is a fundamental medical image analysis task, and a wide variety of
approaches have been proposed. However, only a few studies have comprehensively …

Cellular senescence is a well-established driver of aging and age-related diseases. There
are many challenges to map** senescent cells in tissues such as the absence of specific …

The establishment of image correspondence through robust image registration is critical to
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …

Image registration, the process of aligning two or more images, is the core technique of
many (semi-) automatic medical image analysis tasks. Recent studies have shown that deep …