Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies
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Abstract (arXiv)
Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits. When applied to high-dimensional medical imaging data, a key step is to extract lower-dimensional, yet informative representations of the data as traits. Representation learning for imaging genetics is largely under-explored due to the unique challenges posed by GWAS in comparison to typical visual representation learning. In this study, we tackle this problem from the mutual information (MI) perspective by identifying key limitations of existing methods. We introduce a trans-modal learning framework Genetic InfoMax (GIM), including a regularized MI estimator and a novel genetics-informed transformer to address the specific challenges of GWAS. We evaluate GIM on human brain 3D MRI data and establish standardized evaluation protocols to compare it to existing approaches. Our results demonstrate the effectiveness of GIM and a significantly improved performance on GWAS.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 55 references that are in the map. First posted on arXiv 26 Sep 2023.
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26 of 55 references.
References · 26
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
- 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
- 2020Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hyperspherecited by 1,405
- 2018Learning deep representations by mutual information estimation and maximizationcited by 1,273
- 2018Deep Graph Infomaxcited by 1,106
- 2020What Makes for Good Views for Contrastive Learning?cited by 846
- 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 734
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- 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 571
- 2022Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learningcited by 561
- 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 506
- 2021Understanding Dimensional Collapse in Contrastive Self-supervised Learningcited by 378
- 2020What Should Not Be Contrastive in Contrastive Learningcited by 226
- 2022Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlationscited by 172
- 20213D Infomax improves GNNs for Molecular Property Predictioncited by 146
- 2021Towards the Generalization of Contrastive Self-Supervised Learningcited by 99
- 2021CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representationscited by 72
- 2021ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Geneticscited by 36
- 2021Towards Robust Classification Model by Counterfactual and Invariant Data Generationcited by 30
- 2020Representation Learning with Video Deep InfoMaxcited by 17
- 2020Improving Transformation Invariance in Contrastive Representation Learningcited by 16
- 2020Global Voxel Transformer Networks for Augmented Microscopycited by 6
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