Summary of How Good Are We? Evaluating Cell Ai Foundation Models in Kidney Pathology with Human-in-the-loop Enrichment, by Junlin Guo et al.
How Good Are We? Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment
by Junlin Guo, Siqi Lu, Can Cui, Ruining Deng, Tianyuan Yao, Zhewen Tao, Yizhe Lin, Marilyn Lionts, Quan Liu, Juming Xiong, Yu Wang, Shilin Zhao, Catie Chang, Mitchell Wilkes, Mengmeng Yin, Haichun Yang, Yuankai Huo
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the performance of recent cell foundation models on nuclei segmentation within a single organ, specifically the kidney, using a curated multi-center, multi-disease, and multi-species external testing dataset. The authors evaluate three state-of-the-art cell foundation models (Cellpose, StarDist, and CellViT) and find that all three improve over their baselines with model fine-tuning using enriched data. Interestingly, the baseline model with the highest F1 score does not yield the best segmentation outcomes after fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how good AI models are at finding tiny things in medical pictures of kidneys. The authors looked at three special kinds of AI models and wanted to know if they could do a good job on this task. They found that these models got better when the data was enriched, which means making it more detailed and accurate. This is important because it helps us understand how well these models can work in real-world situations. |
Keywords
» Artificial intelligence » F1 score » Fine tuning
