通过细粒度解耦随机不确定性，加速乳腺组织红外成像
Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging
- 先快速扫描低信息区域，定位高随机不确定性区域
- 识别可由高保真成像修复的不确定性区域，提升成像效率
- 首次在动态图像空间中实现随机不确定性细粒度解耦
无标签化学成像有望提升数字病理学流程，但数据采集速度仍是瓶颈。为此，我们提出一种自适应策略：先快速扫描全组织的低信息（LI）内容，识别高随机不确定性（AU）区域，并选择性地以更高质量重采样，捕捉更高信息（HI）细节。主要挑战在于区分可通过HI成像缓解的高AU区域与无法缓解的区域。由于现有不确定性框架无法分离此类子类别，我们提出基于后验潜在空间分析的细粒度解耦方法，将可解决与不可解决的高AU区域分离。该方法应用于乳腺组织的红外光谱成像，显著提升下游分割性能。这是首个聚焦动态图像空间（LI到HI）中细粒度随机不确定性解耦的研究，开创性应用于病理成像加速。
原文摘要 · Abstract (English)
Label-free chemical imaging holds significant promise for improving digital pathology workflows, but data acquisition speed remains a limiting factor. To address this gap, we propose an adaptive strategy-initially scan the low information (LI) content of the entire tissue quickly, identify regions with high aleatoric uncertainty (AU), and selectively re-image them at better quality to capture higher information (HI) details. The primary challenge lies in distinguishing between high-AU regions mitigable through HI imaging and those that are not. However, since existing uncertainty frameworks cannot separate such AU subcategories, we propose a fine-grained disentanglement method based on post-hoc latent space analysis to unmix resolvable from irresolvable high-AU regions. We apply our approach to streamline infrared spectroscopic imaging of breast tissues, achieving superior downstream segmentation performance. This marks the first study focused on fine-grained AU disentanglement within dynamic image spaces (LI-to-HI), with novel application to streamline histopathology.
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