The robust specification of organ development depends on coordinated cell-cell
communication. This process requires signal integration among multiple pathways, relying …

Reverse-engineering how complex multicellular systems develop and function is a grand
challenge for systems bioengineers. This challenge has motivated the creation of a suite of …

Compression is a standard procedure for making convolutional neural networks (CNNs)
adhere to some specific computing resource constraints. However, searching for a …

The Encoder-Decoder architecture is a main stream deep learning model for biomedical
image segmentation. The encoder fully compresses the input and generates encoded …

Medical image segmentation using deep neural networks has been highly successful.
However, the effectiveness of these networks is often limited by inadequate dense prediction …

Cell tracking in time-lapse microscopy images is important to study biological processes. We
propose a new multi-object tracking method based on a dynamic memory network and …

Accurately identifying objects in biomedical images plays a crucial role in biomedical image
analysis, as it enables various downstream tasks such as cell behavior analysis and cancer …

Biomedical image analysis plays a vital role in disease investigation and computer-aided
diagnosis. Operations like target object segmentation, and classification are principal tools …

Abstract Development of fully functional organs from undifferentiated cells requires constant
communication between various participating cells. An important component in this cellular …

Understanding organ development requires an understanding of how individual cells make
decisions, and how those decisions integrate into system-wide properties. However, in many …