Lymphocyte Detection

Lymphocyte detection in histopathology images is crucial for assessing tumor microenvironments and guiding cancer treatment decisions, particularly in immunotherapy. Current research focuses on developing automated methods using deep learning architectures like U-Nets, YOLOv5, and Vision Transformers, often leveraging readily available Hematoxylin and Eosin (H&E) stained slides instead of more expensive immunohistochemistry. These advancements aim to improve the accuracy, efficiency, and accessibility of lymphocyte quantification, ultimately leading to better prognostication and personalized cancer care. The development of large, automatically generated datasets like Immunocto is also significantly contributing to improved model performance.