In-Context Reverse Classification Accuracy: Efficient Estimation of Segmentation Quality without Ground-Truth

• URL: http://arxiv.org/abs/2503.04522v1
• Date: Thu, 06 Mar 2025 15:08:34 GMT
• Title: In-Context Reverse Classification Accuracy: Efficient Estimation of Segmentation Quality without Ground-Truth
• Authors: Matias Cosarinsky, Ramiro Billot, Lucas Mansilla, Gabriel Gimenez, Nicolas Gaggión, Guanghui Fu, Enzo Ferrante,
• Abstract summary: In this paper, we introduce In-Context Reverse Classification Accuracy (In-Context RCA), a novel framework for automatically estimating segmentation quality.<n>Our approach enables efficient quality estimation with minimal reference data.
• Score: 3.592919051221766
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Assessing the quality of automatic image segmentation is crucial in clinical practice, but often very challenging due to the limited availability of ground truth annotations. In this paper, we introduce In-Context Reverse Classification Accuracy (In-Context RCA), a novel framework for automatically estimating segmentation quality in the absence of ground-truth annotations. By leveraging recent in-context learning segmentation models and incorporating retrieval-augmentation techniques to select the most relevant reference images, our approach enables efficient quality estimation with minimal reference data. Validated across diverse medical imaging modalities, our method demonstrates robust performance and computational efficiency, offering a promising solution for automated quality control in clinical workflows, where fast and reliable segmentation assessment is essential. The code is available at https://github.com/mcosarinsky/In-Context-RCA.

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