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 …

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 …

Detecting various types of cells in and around the tumor matrix holds a special significance
in characterizing the tumor micro-environment for cancer prognostication and research …

In this work we present an experimental setup to semi automatically obtain exhaustive nuclei
labels across 19 different tissue types, and therefore construct a large pan-cancer dataset for …

Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Counting objects in digital images is a process that should be replaced by machines. This
tedious task is time consuming and prone to errors due to fatigue of human annotators. The …

Neural cell instance segmentation, which aims at joint detection and segmentation of every
neural cell in a microscopic image, is essential to many neuroscience applications. The …

Efficient and robust cell detection serves as a critical prerequisite for many subsequent
biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a …

Accurately counting the number of cells in microscopy images is required in many medical
diagnosis and biological studies. This task is tedious, time-consuming, and prone to …