Three-stage medical few-shot classification based on adaptive regularization with HMCE loss

Key Points

• The framework results in enhanced classification accuracy, especially in low-data scenarios, which is critical in medical settings.
• Achieving a notable 15% increase in accuracy can significantly impact diagnostic processes with limited samples.
• The proposed analysis utilizes a three-stage medical few-shot classification method with adaptive regularization and HMCE loss.
• This approach highlights the potential for effective learning in scenarios with sparse data, yet further validation is needed in larger clinical settings.