Bone Marrow

Bone marrow analysis is crucial for diagnosing hematological malignancies like leukemia, but manual microscopic examination is time-consuming and prone to error. Current research focuses on developing automated image analysis methods using deep learning, employing architectures like transformers and convolutional neural networks with attention mechanisms to improve the accuracy and efficiency of cell classification and counting. These advancements leverage both labeled and unlabeled data, addressing challenges like limited annotated datasets and class imbalance, ultimately aiming to improve diagnostic speed and accuracy in hematopathology. The resulting models show promise for streamlining clinical workflows and enhancing the diagnosis of various blood disorders.