A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries
| Autor: | Ngo, Gia H., Nguyen, Minh, Chen, Nancy F., Sabuncu, Mert R. |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Medical Image Analysis. 2022 Jul 19:102540 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.media.2022.102540 |
| Popis: | Neuroimaging studies are often limited by the number of subjects and cognitive processes that can be feasibly interrogated. However, a rapidly growing number of neuroscientific studies have collectively accumulated an extensive wealth of results. Digesting this growing literature and obtaining novel insights remains to be a major challenge, since existing meta-analytic tools are constrained to keyword queries. In this paper, we present Text2Brain, an easy to use tool for synthesizing brain activation maps from open-ended text queries. Text2Brain was built on a transformer-based neural network language model and a coordinate-based meta-analysis of neuroimaging studies. Text2Brain combines a transformer-based text encoder and a 3D image generator, and was trained on variable-length text snippets and their corresponding activation maps sampled from 13,000 published studies. In our experiments, we demonstrate that Text2Brain can synthesize meaningful neural activation patterns from various free-form textual descriptions. Text2Brain is available at https://braininterpreter.com as a web-based tool for efficiently searching through the vast neuroimaging literature and generating new hypotheses. Comment: arXiv admin note: text overlap with arXiv:2109.13814 |
| Databáze: | arXiv |
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