Summary of A Study Of Acquisition Functions For Medical Imaging Deep Active Learning, by Bonaventure F. P. Dossou
A Study of Acquisition Functions for Medical Imaging Deep Active Learning
by Bonaventure F. P. Dossou
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); 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 proposed work showcases the potential of active learning in scenarios where labeled data is scarce, particularly in medical contexts. By exploring different selection criteria (BALD, MeanSTD, and MaxEntropy) on the ISIC 2016 dataset, the study demonstrates that uncertainty can be a valuable indicator for the Melanoma detection task. The results confirm that BALD performs better than other acquisition functions on average, but also highlight class imbalance as a crucial issue in real-world settings. This study provides a foundation for future work to improve its findings and contribute to the advancement of deep learning in medical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how we can use machine learning models to help doctors diagnose skin cancer better. Right now, it’s hard to find labeled data (data that has been marked as either cancerous or not) because it takes a lot of time and money. The researchers tested different ways to select which data points to label first. They found that one method called BALD was the most effective. However, they also noticed that the models were better at detecting non-cancerous skin than cancerous skin, which could be a problem in real-world situations. Overall, this study shows how we can use machine learning to improve medical diagnosis and suggests areas for further research. |
