Supervised vs Unsupervised Learning

Machine learning methods differ mainly in whether training data contains known target labels.

Feature	Supervised Learning	Unsupervised Learning
Training data	Labeled	Unlabeled
Goal	Learn mapping from input to target	Discover structure/patterns
Common tasks	Classification, regression	Clustering, dimensionality reduction
Example in radiology	Tumor vs non-tumor classification	Grouping scans into similar patterns

Supervised Learning

The model is trained using examples where the desired output is known.

For radiology, a dataset may contain CT/MRI images labeled tumor or no tumor. A neural network learns features associated with the labels and can then predict the class of a new image.

Examples:

• CNN for tumor detection.
• Fracture classification.
• Disease-risk prediction.

Unsupervised Learning

The model receives data without predefined labels and attempts to discover structure in the dataset.

For example, clustering could group medical images according to similarities in image features without being told the diagnostic category beforehand.

Examples:

• K-means clustering.
• Hierarchical clustering.
• Principal Component Analysis (PCA) for dimensionality reduction.

Example

Suppose 10,000 chest X-rays are available.

• If each image is labeled pneumonia / normal, training a classifier is supervised learning.
• If no labels are provided and an algorithm groups images according to similarity, it is unsupervised learning.

Conclusion

Supervised learning learns from labeled examples, whereas unsupervised learning discovers patterns in unlabeled data. Both can support medical-imaging research, but clinical deployment requires appropriate validation and human oversight.