Renal digital pathology visual knowledge search platform based on language large model and book knowledge
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929401337217024 |
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| author | Lv, Xiaomin Lai, Chong Ding, Liya Lai, Maode Sun, Qingrong |
| author_facet | Lv, Xiaomin Lai, Chong Ding, Liya Lai, Maode Sun, Qingrong |
| contents | Large models have become mainstream, yet their applications in digital pathology still require exploration. Meanwhile renal pathology images play an important role in the diagnosis of renal diseases. We conducted image segmentation and paired corresponding text descriptions based on 60 books for renal pathology, clustering analysis for all image and text description features based on large models, ultimately building a retrieval system based on the semantic features of large models. Based above analysis, we established a knowledge base of 10,317 renal pathology images and paired corresponding text descriptions, and then we evaluated the semantic feature capabilities of 4 large models, including GPT2, gemma, LLma and Qwen, and the image-based feature capabilities of dinov2 large model. Furthermore, we built a semantic retrieval system to retrieve pathological images based on text descriptions, and named RppD (aidp.zjsru.edu.cn). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18556 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Renal digital pathology visual knowledge search platform based on language large model and book knowledge Lv, Xiaomin Lai, Chong Ding, Liya Lai, Maode Sun, Qingrong Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Large models have become mainstream, yet their applications in digital pathology still require exploration. Meanwhile renal pathology images play an important role in the diagnosis of renal diseases. We conducted image segmentation and paired corresponding text descriptions based on 60 books for renal pathology, clustering analysis for all image and text description features based on large models, ultimately building a retrieval system based on the semantic features of large models. Based above analysis, we established a knowledge base of 10,317 renal pathology images and paired corresponding text descriptions, and then we evaluated the semantic feature capabilities of 4 large models, including GPT2, gemma, LLma and Qwen, and the image-based feature capabilities of dinov2 large model. Furthermore, we built a semantic retrieval system to retrieve pathological images based on text descriptions, and named RppD (aidp.zjsru.edu.cn). |
| title | Renal digital pathology visual knowledge search platform based on language large model and book knowledge |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.18556 |