Summary of Machine Learning and Vision Transformers For Thyroid Carcinoma Diagnosis: a Review, by Yassine Habchi et al.
Machine Learning and Vision Transformers for Thyroid Carcinoma Diagnosis: A review
by Yassine Habchi, Hamza Kheddar, Yassine Himeur, Abdelkrim Boukabou, Ammar Chouchane, Abdelmalik Ouamane, Shadi Atalla, Wathiq Mansoor
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 reviews various machine learning-based approaches for diagnosing thyroid cancer (TC), particularly those using transformers. The authors introduce a new categorization system for these methods based on AI algorithms, computing environments, and goals. They analyze TC datasets by their features and highlight the importance of AI in aiding diagnosis and treatment. The paper also discusses the progress made and ongoing challenges in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are working on smart diagnostic systems to help doctors with incurable diseases. One challenge is identifying thyroid cancer. They’re using machine learning and big data analysis, including transformers, to predict prognosis and risk of malignancy. The article looks at different studies using AI-based approaches, especially those with transformers, for diagnosing TC. It shows how these methods work, what they’re used for, and why they matter. |
Keywords
* Artificial intelligence * Machine learning
