Summary of A New Era in Computational Pathology: a Survey on Foundation and Vision-language Models, by Dibaloke Chanda et al.
A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models
by Dibaloke Chanda, Milan Aryal, Nasim Yahya Soltani, Masoud Ganji
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 presents a comprehensive overview of recent advancements in foundation models (FMs) and vision-language models (VLMs) in computational pathology (CPath). By integrating these models, pathologists can leverage their diagnostic workflow with AI-powered tools. FMs enable learning a representation space adaptable to various downstream tasks without explicit supervision, while VLMs allow pathology reports written in natural language to be used as rich semantic information sources. The survey summarizes tools, datasets, and training schemes for these models, categorizing them into distinct groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper talks about how artificial intelligence is changing the way doctors diagnose diseases by looking at slides under a microscope. It’s like having a superpowerful assistant that helps make diagnoses more accurate and efficient. The researchers are showing how AI can learn from lots of different types of data and use that information to help doctors make better decisions. |
