Summary of Joint-embedding Masked Autoencoder For Self-supervised Learning Of Dynamic Functional Connectivity From the Human Brain, by Jungwon Choi et al.
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain
by Jungwon Choi, Hyungi Lee, Byung-Hoon Kim, Juho Lee
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neurons and Cognition (q-bio.NC)
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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 This paper introduces Spatio-Temporal Joint Embedding Masked Autoencoder (ST-JEMA), a novel generative self-supervised learning technique that addresses challenges in representing dynamic functional connectivity from human brain networks. Building upon the Joint Embedding Predictive Architecture (JEPA) from computer vision, ST-JEMA learns higher-level semantic representations by reconstructing dynamic graphs considering temporal perspectives. The method is evaluated on the UK Biobank dataset and outperforms previous approaches in predicting phenotypes and psychiatric diagnoses across eight benchmark fMRI datasets, even with limited samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to analyze brain scans using machine learning techniques. They created a model called ST-JEMA that can learn from brain scan data without needing a lot of labeled information. This is important because collecting labeled data for brain scans can be difficult and time-consuming. The study shows that the ST-JEMA model is very good at predicting different types of brain activity, such as different personality traits or mental health conditions. |
Keywords
* Artificial intelligence * Autoencoder * Embedding * Machine learning * Self supervised
