Summary of Classification Of Breast Cancer Histopathology Images Using a Modified Supervised Contrastive Learning Method, by Matina Mahdizadeh Sani et al.
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning Method
by Matina Mahdizadeh Sani, Ali Royat, Mahdieh Soleymani Baghshah
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel approach to improve the robustness of deep neural networks in medical image processing tasks by addressing the overfitting issue when faced with limited data. The method combines supervised contrastive learning with self-supervised pre-training and introduces a two-stage strategy to reduce false positives and negatives. The authors evaluate their approach on the BreakHis dataset, achieving an increase in classification accuracy of 1.45% compared to the state-of-the-art method, resulting in an absolute accuracy of 93.63%. This paper demonstrates the effectiveness of leveraging data properties to learn more appropriate representation spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how artificial intelligence can be used to improve medical image analysis. It shows a new way to make AI models less likely to overfit and more accurate when there is limited training data. The researchers tested their approach on images of breast cancer tissue and got a 1.45% improvement in accuracy, which is quite significant. |
Keywords
» Artificial intelligence » Classification » Overfitting » Self supervised » Supervised
