Summary of Pathinsight: Instruction Tuning Of Multimodal Datasets and Models For Intelligence Assisted Diagnosis in Histopathology, by Xiaomin Wu et al.
PathInsight: Instruction Tuning of Multimodal Datasets and Models for Intelligence Assisted Diagnosis in Histopathology
by Xiaomin Wu, Rui Xu, Pengchen Wei, Wenkang Qin, Peixiang Huang, Ziheng Li, Lin Luo
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 multimodal large models have simplified the integration of image analysis with textual descriptions, but there’s a significant divide between cutting-edge technology and its application in clinical settings due to high training costs and scarcity of high-quality datasets. Researchers compiled a dataset of approximately 45,000 cases covering six tasks, including classification of organ tissues, generating pathology report descriptions, and addressing questions and answers. They fine-tuned models LLaVA, Qwen-VL, and InternLM with this dataset to enhance instruction-based performance. The evaluation results demonstrate the fine-tuned model’s proficiency in addressing typical pathological questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pathologists use computers to help diagnose diseases. There are big machines that can look at pictures of organs and write descriptions. But these machines are very expensive and need a lot of training data. Researchers made a special set of training data with 45,000 cases covering different tasks like identifying organ tissues and writing report summaries. They used this data to improve the performance of three computer models: LLaVA, Qwen-VL, and InternLM. The results show that these improved models can answer typical questions about diseases. |
Keywords
» Artificial intelligence » Classification
