Part 8/11:

• Limited Labeled Data: High-quality annotations require expert radiologists, whose time is scarce and expensive. The availability of annotated datasets (often ranging from hundreds to thousands of images) remains a bottleneck for training robust models.

• Privacy Concerns: Patient data is sensitive, and sharing clinical images requires stringent privacy safeguards. Ethical considerations, masking techniques, and data anonymization are critical yet complicate data acquisition.

• Generalizability: Models trained on data from one institution or population may not perform well elsewhere. Ensuring adaptability across different scanners, protocols, and demographics is an ongoing challenge.