Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915364879728640 |
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| author | Oh, Ji-Hun Falahkheirkhah, Kianoush Bhargava, Rohit |
| author_facet | Oh, Ji-Hun Falahkheirkhah, Kianoush Bhargava, Rohit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20532 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging Oh, Ji-Hun Falahkheirkhah, Kianoush Bhargava, Rohit Computer Vision and Pattern Recognition 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. |
| title | Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.20532 |