Emily Clarke

What is Histology?

Histology is an often misunderstood branch of biology. It's the science of tissue and is sometimes known as "microscopic anatomy." Histologists study and analyze tissue on a microscopic level, gaining more insight into tissue health and understanding the changes it undergoes.

Histologists are medical scientists who often work behind the scenes to test tissue samples. They can spot potential issues on a cellular level, identifying everything from cancer cells to disease-caused damage. If you've ever gotten a biopsy, there's a good chance that a histologist analyzed it. Their work is integral in the diagnostic stages of healthcare, giving medical providers the information they need to provide an accurate diagnosis.

Technologies that Benefit Histology

In the past, the only way for specialists to understand the health of human tissue was to look at it under a microscope and use their knowledge to make critical judgments. That still remains true today, but emerging technologies are revolutionizing the field.

Histology AI model development is adding a new level of accuracy to the healthcare space. Artificial intelligence can help providers gain more insight into human tissue than ever before through computer vision.

Computer vision and machine learning train computers to interpret the data they see. Instead of relying solely on the opinion of histologists and medical providers, these systems can provide clarity and make it easier to make sense of microscopic imaging.

Computer vision "sees" images and makes predictions on what it sees.

Before these models can deploy, they require histology AI model development and extensive training.

During training, operators will feed annotated datasets into a machine-learning model. As the model processes those datasets, it slowly learns to make accurate predictions. The more training it receives and the more precise the labeling, the better it performs.

Efficient histology image labeling is paramount. Computer vision and machine learning are only as accurate as the datasets it uses to train. That's why many operators invest in active learning platforms. The right platform can improve annotation efficiency, making it easier to train computer vision systems that will play a part in helping patients get the care they need.

Author Resource:-

Emily Clarke writes about tech for automated annotation, AI labeling, data evaluation and more. You can find her thoughts at AI labeling blog.