Summary of Generation Of Synthetic Data Using Breast Cancer Dataset and Classification with Resnet18, by Dilsat Berin Aytar and Semra Gunduc
Generation of synthetic data using breast cancer dataset and classification with resnet18
by Dilsat Berin Aytar, Semra Gunduc
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed paper develops a Generative Adversarial Network (GAN) model called MSG-GAN for generating synthetic patch images of breast histopathology datasets. The goal is to aid in cancer identification, as real data collection can be expensive and privacy concerns exist. The MSG-GAN model generates malignant and benign synthetic images that mimic the real dataset. A ResNet18 model with transfer learning is used to classify both synthetic and real data. The paper investigates whether the synthetic images behave similarly to real data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to generate fake medical images that can help doctors diagnose breast cancer more accurately. They used a special kind of artificial intelligence called Generative Adversarial Networks (GANs) to create these fake images, which are very similar to real ones. This is important because it’s hard and expensive to collect real medical data, and it’s also private information. The researchers hope that their new method will help doctors diagnose breast cancer more quickly and accurately. |
Keywords
» Artificial intelligence » Gan » Generative adversarial network » Transfer learning
