Summary of Advancing Melanoma Diagnosis with Self-supervised Neural Networks: Evaluating the Effectiveness Of Different Techniques, by Srivishnu Vusirikala et al.
Advancing Melanoma Diagnosis with Self-Supervised Neural Networks: Evaluating the Effectiveness of Different Techniques
by Srivishnu Vusirikala, Suraj Rajendran
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 investigates the potential of self-supervision techniques in improving the accuracy of convolutional neural networks (CNNs) trained for melanoma patch classification. Various self-supervision methods, including rotation prediction, missing patch prediction, and corruption removal, are implemented and evaluated for their impact on CNN performance. Preliminary results show a positive influence of self-supervision on model accuracy, with the corruption removal method demonstrating notable efficacy. While improvements are observed, further enhancement is possible through training over more epochs or expanding the dataset. Future research directions include exploring methods like Bootstrap Your Own Latent (BYOL) and contrastive learning, while considering the cost-benefit trade-off due to their resource-intensive nature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how self-supervision can help improve deep learning models that classify melanoma patches. The researchers try different techniques, such as predicting rotations or missing patches, and see if they make a difference. So far, it looks like these methods can help the models get better at detecting melanoma. This is exciting because it could help doctors detect melanoma more accurately. However, there’s still room for improvement, so the researchers suggest trying new techniques in the future to see what works best. |
Keywords
* Artificial intelligence * Classification * Cnn * Deep learning
