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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.19378 |
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| _version_ | 1866908798914920448 |
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| author | Xu, Ziyang Lin, Mingquan Zhou, Yiliang Xu, Zihan Orlow, Seth J. Meehan, Shane A. Flamm, Alexandra Moshiri, Ata S. Peng, Yifan |
| author_facet | Xu, Ziyang Lin, Mingquan Zhou, Yiliang Xu, Zihan Orlow, Seth J. Meehan, Shane A. Flamm, Alexandra Moshiri, Ata S. Peng, Yifan |
| contents | Accessing high-quality, open-access dermatopathology image datasets for learning and cross-referencing is a common challenge for clinicians and dermatopathology trainees. To establish a comprehensive open-access dermatopathology dataset for educational, cross-referencing, and machine-learning purposes, we employed a hybrid workflow to curate and categorize images from the PubMed Central (PMC) repository. We used specific keywords to extract relevant images, and classified them using a novel hybrid method that combined deep learning-based image modality classification with figure caption analyses. Validation on 651 manually annotated images demonstrated the robustness of our workflow, with an F-score of 89.6% for the deep learning approach, 61.0% for the keyword-based retrieval method, and 90.4% for the hybrid approach. We retrieved over 7,772 images across 166 diagnoses and released this fully annotated dataset, reviewed by board-certified dermatopathologists. Using our dataset as a challenging task, we found the current image analysis algorithm from OpenAI inadequate for analyzing dermatopathology images. In conclusion, we have developed a large, peer-reviewed, open-access dermatopathology image dataset, DermpathNet, which features a semi-automated curation workflow. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19378 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Establishing dermatopathology encyclopedia DermpathNet with Artificial Intelligence-Based Workflow Xu, Ziyang Lin, Mingquan Zhou, Yiliang Xu, Zihan Orlow, Seth J. Meehan, Shane A. Flamm, Alexandra Moshiri, Ata S. Peng, Yifan Computer Vision and Pattern Recognition Accessing high-quality, open-access dermatopathology image datasets for learning and cross-referencing is a common challenge for clinicians and dermatopathology trainees. To establish a comprehensive open-access dermatopathology dataset for educational, cross-referencing, and machine-learning purposes, we employed a hybrid workflow to curate and categorize images from the PubMed Central (PMC) repository. We used specific keywords to extract relevant images, and classified them using a novel hybrid method that combined deep learning-based image modality classification with figure caption analyses. Validation on 651 manually annotated images demonstrated the robustness of our workflow, with an F-score of 89.6% for the deep learning approach, 61.0% for the keyword-based retrieval method, and 90.4% for the hybrid approach. We retrieved over 7,772 images across 166 diagnoses and released this fully annotated dataset, reviewed by board-certified dermatopathologists. Using our dataset as a challenging task, we found the current image analysis algorithm from OpenAI inadequate for analyzing dermatopathology images. In conclusion, we have developed a large, peer-reviewed, open-access dermatopathology image dataset, DermpathNet, which features a semi-automated curation workflow. |
| title | Establishing dermatopathology encyclopedia DermpathNet with Artificial Intelligence-Based Workflow |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.19378 |