Tumor Infiltrating Lymphocyte

Tumor-infiltrating lymphocytes (TILs) are immune cells within tumors, and their quantification is crucial for predicting cancer prognosis and treatment response. Current research heavily focuses on developing automated, computationally efficient methods for TIL detection and quantification in whole slide images (WSIs) using deep learning architectures like Efficient-UNet, RTMDet, and HoVer-Net, often incorporating techniques like patch sampling and transfer learning to improve accuracy and reduce resource demands. These advancements aim to replace laborious manual analysis, providing more objective and consistent TIL scoring for improved patient stratification and personalized medicine, particularly in cancers like breast and lung cancer. While promising, further clinical validation is needed to ensure widespread adoption in clinical practice.