IHC Stained Image

Immunohistochemistry (IHC)-stained images are crucial in pathology for diagnosing and treating cancer, but their manual analysis is subjective and time-consuming. Current research focuses on developing automated image analysis tools, primarily using deep learning models like instance segmentation and convolutional neural networks, to quantify biomarkers and improve the accuracy and reproducibility of IHC scoring across various cancer types and stains. These advancements aim to standardize IHC assessment, leading to more consistent diagnoses, improved treatment decisions, and ultimately better patient outcomes. The development of online platforms further facilitates accessibility and collaboration in this field.