Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929310986665984 |
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| author | Aubreville, Marc Ganz, Jonathan Ammeling, Jonas Kaltenecker, Christopher C. Bertram, Christof A. |
| author_facet | Aubreville, Marc Ganz, Jonathan Ammeling, Jonas Kaltenecker, Christopher C. Bertram, Christof A. |
| contents | The QUILT-1M dataset is the first openly available dataset containing images harvested from various online sources. While it provides a huge data variety, the image quality and composition is highly heterogeneous, impacting its utility for text-conditional image synthesis. We propose an automatic pipeline that provides predictions of the most common impurities within the images, e.g., visibility of narrators, desktop environment and pathology software, or text within the image. Additionally, we propose to use semantic alignment filtering of the image-text pairs. Our findings demonstrate that by rigorously filtering the dataset, there is a substantial enhancement of image fidelity in text-to-image tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis Aubreville, Marc Ganz, Jonathan Ammeling, Jonas Kaltenecker, Christopher C. Bertram, Christof A. Computer Vision and Pattern Recognition Artificial Intelligence The QUILT-1M dataset is the first openly available dataset containing images harvested from various online sources. While it provides a huge data variety, the image quality and composition is highly heterogeneous, impacting its utility for text-conditional image synthesis. We propose an automatic pipeline that provides predictions of the most common impurities within the images, e.g., visibility of narrators, desktop environment and pathology software, or text within the image. Additionally, we propose to use semantic alignment filtering of the image-text pairs. Our findings demonstrate that by rigorously filtering the dataset, there is a substantial enhancement of image fidelity in text-to-image tasks. |
| title | Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2404.07676 |