Semantic segmentation is a computer vision task that involves identifying and labeling different objects or regions within an image. Accurate segmentation is crucial in various medical applications, such as tumor detection and disease diagnosis. However, detecting failures in semantic segmentation can be challenging due to the complexity of the task. In this article, we propose a novel framework for failure detection in semantic segmentation, which utilizes a hybrid approach combining different measures to evaluate segmentation quality.
Hybrid Approach
Our proposed framework combines multiple disparate measures to evaluate segmentation quality. These measures include prediction error meta classification, aggregated dispersion measures of softmax probabilities, and unsupervised methods such as image segmentation evaluation using a survey of unsupervised methods. By combining these measures, we can detect failures in semantic segmentation more accurately than with any single measure alone.
Utilization of SAM
To further improve the accuracy of failure detection, we utilize a score alignment method (SAM) to align scores from different measures. This alignment allows us to combine the strengths of each measure and create a more comprehensive evaluation of segmentation quality. By comparing the output of a probabilistic segmentation model with the actual segmentation, we can detect failures in a more robust and reliable manner.
Experiments
We evaluated our proposed framework using several experiments. Our results showed that the scores computed using SAM exhibited strong positive correlation (Pearson correlation and Spearman correlation) with Dice coefficient scores reflecting the true segmentation quality. This demonstrates the effectiveness of our approach in detecting failures in semantic segmentation.
Conclusion
In conclusion, this article proposed a novel framework for failure detection in semantic segmentation using a hybrid approach combining multiple disparate measures. Our proposed framework utilizes SAM to align scores from different measures and improve the accuracy of failure detection. The results of our experiments demonstrate the effectiveness of our approach in detecting failures in semantic segmentation, highlighting its potential application in various medical applications. By providing a comprehensive evaluation of segmentation quality, our framework can help improve the accuracy and reliability of semantic segmentation in the field of computer vision.
