Nucleus Detection

Nucleus detection in microscopy images is crucial for various applications in pathology and medical image analysis, aiming to automatically identify and classify individual cell nuclei within tissue samples. Current research emphasizes developing robust and generalizable methods, focusing on architectures like transformers and employing techniques such as prompt learning, self-supervised pretraining, and weakly-supervised learning to address challenges posed by data scarcity, annotation inconsistencies, and diverse image characteristics. These advancements enable more efficient and accurate analysis of histopathology images, facilitating improved disease diagnosis, drug discovery, and personalized medicine.