What are the Challenges of Implementing AutoML Vision in Histology?
Despite its potential, there are several challenges associated with implementing AutoML Vision in histology:
Data Quality: The accuracy of AutoML Vision models depends on the quality of the training data. Poor-quality images or annotations can lead to inaccurate predictions. Interpretability: Machine learning models, especially deep learning models, can be difficult to interpret. Understanding why a model made a specific prediction is crucial in a clinical setting. Integration: Integrating AutoML Vision into existing workflows and systems can be challenging, requiring significant changes to established processes. Regulatory Approval: Any automated system used in a clinical setting must undergo rigorous validation and obtain regulatory approval, which can be a lengthy process.
