Neighbor-aware calibration of segmentation networks with penalty-based constraints

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Reliable confidence scores from deep neural networks are crucial for critical applications like healthcare.
• Existing methods for calibrating deep segmentation networks often overlook local object structure by focusing on individual pixels.
• Spatially Varying Label Smoothing (SVLS) considers spatial relationships but lacks a balance mechanism for optimization.

Purpose of the Study:

• To analyze the limitations of Spatially Varying Label Smoothing (SVLS) in deep segmentation network calibration.
• To propose a novel, principled calibration method, Neighbor Aware Calibration (NACL), that addresses SVLS's shortcomings.
• To demonstrate NACL's superior performance and model-agnostic nature in segmentation tasks.

Main Methods:

• Presented a constrained optimization perspective of SVLS, revealing implicit constraints on soft class proportions.
• Proposed NACL, a method utilizing equality constraints on logit values for explicit control over calibration.
• Conducted comprehensive experiments on diverse segmentation benchmarks.

Main Results:

• NACL demonstrates superior calibration performance compared to existing methods.
• The proposed approach maintains the discriminative power of deep segmentation networks.
• Ablation studies confirm NACL's model-agnostic applicability across various deep segmentation architectures.

Conclusions:

• NACL provides a flexible and effective solution for improving confidence scores in deep segmentation networks.
• The method enhances calibration without compromising segmentation accuracy.
• NACL is a versatile tool applicable to a wide range of deep segmentation models.