ArticleNeuroImage2022
Self-supervised Natural Image Reconstruction and Large-scale Semantic Classification from Brain Activity.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural Image Reconstruction From fMRI Using Deep Learning: A Survey.Frontiers in neuroscience · 2021Pooled it
- Preferred visual experiences provide intuitive descriptions of the functional properties of the cortical navigation network.bioRxiv : the preprint server for biology · 2026Article
- Article
- Functional diversity of visual cortex improves constraint-free natural image reconstruction from human brain activity.Fundamental research · 2025Article
- DCBAN: A Dynamic Confidence Bayesian Adaptive Network for Reconstructing Visual Images from fMRI Signals.Brain sciences · 2025Article
- Sleuthing subjectivity: a review of covert measures of consciousness.Nature reviews. Neuroscience · 2025Review
- Improved image reconstruction from brain activity through automatic image captioning.Scientific reports · 2025Article
- Efficient Neural Decoding Based on Multimodal Training.Brain sciences · 2024Article
- Computational reconstruction of mental representations using human behavior.Nature communications · 2024Article
- Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain.PLoS computational biology · 2024Article
- RECONSTRUCTING RETINAL VISUAL IMAGES FROM 3T FMRI DATA ENHANCED BY UNSUPERVISED LEARNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Natural Image Reconstruction from fMRI Based on Node-Edge Interaction and Multi-Scale Constraint.Brain sciences · 2024Article
- Through their eyes: Multi-subject brain decoding with simple alignment techniques.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Article
- Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI.Bioengineering (Basel, Switzerland) · 2023Article
- Natural scene reconstruction from fMRI signals using generative latent diffusion.Scientific reports · 2023Article
- EEG-based BCI Dataset of Semantic Concepts for Imagination and Perception Tasks.Scientific data · 2023Article
- The brain-inspired decoder for natural visual image reconstruction.Frontiers in neuroscience · 2023Article
- Reconstruction of perceived face images from brain activities based on multi-attribute constraints.Frontiers in neuroscience · 2022Article
- Image Semantic Recognition and Segmentation Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network.Computational intelligence and neuroscience · 2022Article
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7 authors.
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Abstract
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We present a novel self-supervised approach that goes well beyond the scarce paired data, for achieving both: (i) state-of-the art fMRI-to-image reconstruction, and (ii) first-ever large-scale semantic classification from fMRI responses. By imposing cycle consistency between a pair of deep neural networks (from image-to-fMRI & from fMRI-to-image), we train our image reconstruction network on a large number of "unpaired" natural images (images without fMRI recordings) from many novel semantic categories. This enables to adapt our reconstruction network to a very rich semantic coverage without requiring any explicit semantic supervision. Specifically, we find that combining our self-supervised training with high-level perceptual losses, gives rise to new reconstruction & classification capabilities. In particular, this perceptual training enables to classify well fMRIs of never-before-seen semantic classes, without requiring any class labels during training. This gives rise to: (i) Unprecedented image-reconstruction from fMRI of never-before-seen images (evaluated by image metrics and human testing), and (ii) Large-scale semantic classification of categories that were never-before-seen during network training. Such large-scale (1000-way) semantic classification from fMRI recordings has never been demonstrated before. Finally, we provide evidence for the biological consistency of our learned model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.