Summary of Foundational Models For Pathology and Endoscopy Images: Application For Gastric Inflammation, by Hamideh Kerdegari et al.
Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation
by Hamideh Kerdegari, Kyle Higgins, Dennis Veselkov, Ivan Laponogov, Inese Polaka, Miguel Coimbra, Junior Andrea Pescino, Marcis Leja, Mario Dinis-Ribeiro, Tania Fleitas Kanonnikoff, Kirill Veselkov
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper explores the integration of artificial intelligence (AI) in medical diagnostics, specifically focusing on gastric cancer (GC). The authors discuss how chronic inflammation can lead to changes in the mucosa, such as atrophy, intestinal metaplasia (IM), dysplasia, and ultimately cancer. To enhance the accuracy of endoscopy and pathology image analysis, they propose using foundation models (FM), which are machine or deep learning models trained on diverse data applicable to broad use cases. The review covers recent advancements, applications, and challenges associated with FM in endoscopy and pathology imaging, discussing core principles, architectures, training methodologies, and the pivotal role of large-scale data in developing predictive capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper talks about how artificial intelligence can help doctors detect cancer earlier and more accurately. It focuses on a type of cancer called gastric cancer, which is caused by chronic inflammation that can lead to changes in the mucosa, such as atrophy, intestinal metaplasia (IM), dysplasia, and ultimately cancer. The authors suggest using machine learning models trained on large amounts of data to analyze images taken during endoscopy and improve diagnostic accuracy. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
