Summary of Conpro: Learning Severity Representation For Medical Images Using Contrastive Learning and Preference Optimization, by Hong Nguyen et al.
ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization
by Hong Nguyen, Hoang Nguyen, Melinda Chang, Hieu Pham, Shrikanth Narayanan, Michael Pazzani
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 A novel representation learning method called Con-PrO is proposed for assessing the severity of conditions shown in medical images using Contrastive learningintegrated Preference Optimization. Unlike conventional contrastive learning methods that focus on maximizing the distance between classes, ConPrO injects the distance preference knowledge between various severity classes and the normal class into the latent vector. The paper examines the key components of the framework to understand how contrastive prediction tasks acquire valuable representations. Experimental results show that ConPrO outperforms previous state-of-the-art methods on classification tasks, achieving a 6% and 20% relative improvement compared to supervised and self-supervised baselines, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical images play a crucial role in diagnosis and treatment planning. But understanding the severity of conditions shown in these images is essential for clinical assessment, treatment, and monitoring progression over time. A new approach called Con-PrO helps analyze medical images by combining contrastive learning with preference optimization. This method is better than previous approaches at determining the severity of conditions, allowing doctors to make more accurate diagnoses and develop personalized treatments. |
Keywords
* Artificial intelligence * Classification * Optimization * Representation learning * Self supervised * Supervised
