Summary of Optimizing Contrastive Learning For Cortical Folding Pattern Detection, by Aymeric Gaudin (1) et al.
Optimizing contrastive learning for cortical folding pattern detection
by Aymeric Gaudin, Louise Guillon, Clara Fischer, Arnaud Cachia, Denis Rivière, Jean-François Mangin, Joël Chavas
First submitted to arxiv on: 31 Jan 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 paper presents a novel approach to characterizing cortical folding patterns in the human brain using self-supervised deep learning techniques. The researchers aim to develop a model that can automatically detect and classify various folding patterns, particularly in the cingulate region, which has been linked to schizophrenia characteristics. To achieve this goal, they train a contrastive self-supervised model (SimCLR) on two large MRI image datasets: Human Connectome Project (1101 subjects) and UKBioBank (21070 subjects). The team explores different backbone architectures for SimCLR, including convolutional networks, DenseNets, and PointNets. They evaluate the performance of their model using a linear classification task on a manually labeled dataset, achieving a test AUC of 0.76 with a specific configuration. This study marks the first time that self-supervised deep learning has been applied to cortical skeletons on such a large scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how our brains are structured and how this affects who we are as individuals. Researchers used big computers to look at brain scans of many people and teach the computer to recognize patterns in the brain’s folds. They found that some patterns were linked to certain characteristics or conditions, like schizophrenia. This is important because it could help us develop new ways to diagnose or understand these conditions. The scientists used a special kind of computer program called SimCLR, which learned from lots of brain scans and didn’t need any extra training data. This made the study very large-scale and efficient. |
Keywords
* Artificial intelligence * Auc * Classification * Deep learning * Self supervised
