U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Summary Imaging Mass Cytometry (IMC) is a novel, high multiplexing imaging platform
capable of simultaneously detecting and visualizing up to 40 different protein targets. It is a …

Abstract Fully Convolution Networks have recently become popular for tackling semantic
segmentation problems. However, its performance is dependent on the hyper-parameters it …

In computer vision, particularly in label categorization, attributing features such as color,
shape, and tissue size to each category presents a formidable challenge. Dense features …

Deep convolutional neural networks have been highly effective in segmentation tasks.
However, segmentation becomes more difficult when training images include many complex …

Examination of tissue biopsy and quantification of the various characteristics of cellular
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …

This study introduces a new strategy that combines unsupervised learning (clustering) and
transfer learning. Clustering methods are employed to generate synthetic labels for the …

Cell segmentation is a cornerstone of many bioimage informatics studies, and inaccurate
segmentation introduces error in downstream analysis. Evaluating segmentation results is …

Background and objectives Cytology is a proven, minimally-invasive cancer screening and
surveillance strategy. Given the high incidence of oral cancer globally, there is a need to …

As the rise in cancer cases, there is an increasing demand to develop accurate and rapid
diagnostic tools for early intervention. Pathologists are looking to augment manual analysis …