What are the Challenges in Implementing Machine Learning in Histology?
Despite its advantages, there are several challenges to consider:
1. Data Quality: High-quality, annotated datasets are essential for training effective ML models. Variability in staining techniques and imaging conditions can affect model performance. 2. Interpretability: Understanding how ML algorithms make decisions is crucial for gaining trust in their predictions. Deep learning models, in particular, are often viewed as "black boxes." 3. Integration: Incorporating ML tools into existing histological workflows requires careful planning and validation to ensure compatibility and accuracy. 4. Ethical and Regulatory Issues: The use of ML in clinical diagnostics raises ethical and regulatory concerns, such as data privacy and the need for rigorous validation before clinical deployment.
