利用光学相干断层扫描的中间重建结果，提升医学图像分割精度与不确定性估计。
Exploiting Intermediate Reconstructions in Optical Coherence Tomography for Test-Time Adaption of Medical Image Segmentation
- 通过调节归一化层参数，利用重建过程中的中间表示进行测试时自适应。
- 在3个医学图像数据集上平均提升1.8%分割精度，同时生成无额外成本的不确定性估计。
- 适用于低资源医疗设备场景，适合关注模型可靠性与部署灵活性的研究者。
基层医疗常依赖低成本成像设备用于筛查。为保障诊断准确，这些系统依赖先进重建算法，以逼近高质量设备的性能。此类算法通常采用结合领域先验知识的迭代重建方法。然而，下游任务性能通常仅基于最终重建图像评估，忽略了重建过程中产生的丰富中间表示。本文提出IRTTA，通过一个条件于当前重建时间步的调制网络，在测试时自适应地调整冻结的下游网络归一化层参数，利用中间表示实现性能提升。调制网络通过各时间步的平均熵损失在测试时学习。不同时间步的分割结果差异还自然提供不确定性估计，无需额外开销。该方法在不修改重建流程或下游模型的前提下，提升了分割性能并实现了语义合理的不确定性估计。
原文摘要 · Abstract (English)
Primary health care frequently relies on low-cost imaging devices, which are commonly used for screening purposes. To ensure accurate diagnosis, these systems depend on advanced reconstruction algorithms designed to approximate the performance of high-quality counterparts. Such algorithms typically employ iterative reconstruction methods that incorporate domain-specific prior knowledge. However, downstream task performance is generally assessed using only the final reconstructed image, thereby disregarding the informative intermediate representations generated throughout the reconstruction process. In this work, we propose IRTTA to exploit these intermediate representations at test-time by adapting the normalization-layer parameters of a frozen downstream network via a modulator network that conditions on the current reconstruction timescale. The modulator network is learned during test-time using an averaged entropy loss across all individual timesteps. Variation among the timestep-wise segmentations additionally provides uncertainty estimates at no extra cost. This approach enhances segmentation performance and enables semantically meaningful uncertainty estimation, all without modifying either the reconstruction process or the downstream model.
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