What Challenges Exist in Implementing CNNs in Histology?
Despite the benefits, several challenges need to be addressed:
Data Quality: High-quality, annotated histological images are required to train CNNs effectively. Poor quality data can lead to inaccurate results. Computational Resources: Training and deploying CNNs require significant computational power, which can be a limiting factor for some institutions. Interpretability: CNNs are often seen as 'black boxes', making it difficult to understand how they arrive at specific conclusions. This can be a barrier to clinical acceptance. Regulatory Approval: The use of AI in medical diagnosis must comply with regulatory standards, which can be a lengthy process.
