What Are the Challenges of Implementing Computer Vision in Histology?
Despite its potential, several challenges need to be addressed:
Data Quality: High-quality, well-annotated datasets are essential for training accurate models, but such datasets are often scarce. Variability: Variations in tissue samples, staining techniques, and imaging conditions can affect the performance of computer vision algorithms. Interpretability: Machine learning models, especially deep learning, are often seen as "black boxes," making it difficult to interpret their decisions. Integration: Integrating computer vision systems into existing clinical workflows can be challenging and requires careful planning and validation.
