通过可解释的集成方法，提升医学影像模型的可靠性判断能力。
SASWISE-UE: Segmentation and Synthesis with Interpretable Scalable Ensembles for Uncertainty Estimation
- 从单个训练好的模型生成多个子模型，融合输出并基于差异估计不确定性。
- 分割任务Dice达0.814，图像合成误差降至88.17 HU（原为89.43 HU）。
- 适用于卷积与Transformer模型，适合临床需可信推理的医学影像场景。
本文提出一种高效子模型集成框架，旨在提升医学深度学习模型的可解释性，增强其临床适用性。通过生成不确定性图，使终端用户能够评估模型输出的可靠性。该方法基于单一预训练检查点，通过从同一输入生成多个输出、融合结果并依据输出差异估算不确定性，构建模型族。在CT体部分割和MR-CT图像合成数据集上测试，分割任务平均Dice系数达0.814，图像合成均方误差为88.17 HU（剪枝后由89.43 HU改善）。在噪声和欠采样条件下，不确定性与误差仍保持相关性，验证了其鲁棒性。结果表明，该方法在保持高性能的同时，通过有效不确定性估计显著提升可解释性，适用于多种成像任务中基于卷积或Transformer的模型。
原文摘要 · Abstract (English)
This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to evaluate the reliability of model outputs. We developed a strategy to develop diverse models from a single well-trained checkpoint, facilitating the training of a model family. This involves producing multiple outputs from a single input, fusing them into a final output, and estimating uncertainty based on output disagreements. Implemented using U-Net and UNETR models for segmentation and synthesis tasks, this approach was tested on CT body segmentation and MR-CT synthesis datasets. It achieved a mean Dice coefficient of 0.814 in segmentation and a Mean Absolute Error of 88.17 HU in synthesis, improved from 89.43 HU by pruning. Additionally, the framework was evaluated under corruption and undersampling, maintaining correlation between uncertainty and error, which highlights its robustness. These results suggest that the proposed approach not only maintains the performance of well-trained models but also enhances interpretability through effective uncertainty estimation, applicable to both convolutional and transformer models in a range of imaging tasks.
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