HistoART: Histopathology Artifact Detection and Reporting Tool
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915366167379968 |
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| author | Kahaki, Seyed Webber, Alexander R. Zamzmi, Ghada Subbaswamy, Adarsh Deshpande, Rucha Badano, Aldo |
| author_facet | Kahaki, Seyed Webber, Alexander R. Zamzmi, Ghada Subbaswamy, Adarsh Deshpande, Rucha Badano, Aldo |
| contents | In modern cancer diagnostics, Whole Slide Imaging (WSI) is widely used to digitize tissue specimens for detailed, high-resolution examination; however, other diagnostic approaches, such as liquid biopsy and molecular testing, are also utilized based on the cancer type and clinical context. While WSI has revolutionized digital histopathology by enabling automated, precise analysis, it remains vulnerable to artifacts introduced during slide preparation and scanning. These artifacts can compromise downstream image analysis. To address this challenge, we propose and compare three robust artifact detection approaches for WSIs: (1) a foundation model-based approach (FMA) using a fine-tuned Unified Neural Image (UNI) architecture, (2) a deep learning approach (DLA) built on a ResNet50 backbone, and (3) a knowledge-based approach (KBA) leveraging handcrafted features from texture, color, and frequency-based metrics. The methods target six common artifact types: tissue folds, out-of-focus regions, air bubbles, tissue damage, marker traces, and blood contamination. Evaluations were conducted on 50,000+ image patches from diverse scanners (Hamamatsu, Philips, Leica Aperio AT2) across multiple sites. The FMA achieved the highest patch-wise AUROC of 0.995 (95% CI [0.994, 0.995]), outperforming the ResNet50-based method (AUROC: 0.977, 95% CI [0.977, 0.978]) and the KBA (AUROC: 0.940, 95% CI [0.933, 0.946]). To translate detection into actionable insights, we developed a quality report scorecard that quantifies high-quality patches and visualizes artifact distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HistoART: Histopathology Artifact Detection and Reporting Tool Kahaki, Seyed Webber, Alexander R. Zamzmi, Ghada Subbaswamy, Adarsh Deshpande, Rucha Badano, Aldo Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning In modern cancer diagnostics, Whole Slide Imaging (WSI) is widely used to digitize tissue specimens for detailed, high-resolution examination; however, other diagnostic approaches, such as liquid biopsy and molecular testing, are also utilized based on the cancer type and clinical context. While WSI has revolutionized digital histopathology by enabling automated, precise analysis, it remains vulnerable to artifacts introduced during slide preparation and scanning. These artifacts can compromise downstream image analysis. To address this challenge, we propose and compare three robust artifact detection approaches for WSIs: (1) a foundation model-based approach (FMA) using a fine-tuned Unified Neural Image (UNI) architecture, (2) a deep learning approach (DLA) built on a ResNet50 backbone, and (3) a knowledge-based approach (KBA) leveraging handcrafted features from texture, color, and frequency-based metrics. The methods target six common artifact types: tissue folds, out-of-focus regions, air bubbles, tissue damage, marker traces, and blood contamination. Evaluations were conducted on 50,000+ image patches from diverse scanners (Hamamatsu, Philips, Leica Aperio AT2) across multiple sites. The FMA achieved the highest patch-wise AUROC of 0.995 (95% CI [0.994, 0.995]), outperforming the ResNet50-based method (AUROC: 0.977, 95% CI [0.977, 0.978]) and the KBA (AUROC: 0.940, 95% CI [0.933, 0.946]). To translate detection into actionable insights, we developed a quality report scorecard that quantifies high-quality patches and visualizes artifact distributions. |
| title | HistoART: Histopathology Artifact Detection and Reporting Tool |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.00044 |