From 240453fb0a4566c1352108f36e828e524b139444 Mon Sep 17 00:00:00 2001 From: Ahmed Allam Date: Thu, 12 Oct 2023 23:52:08 +0300 Subject: [PATCH] Updated README.md --- README.md | 4 +++ data/README.md | 82 ------------------------------------------------- data/fmri.gif | Bin 1773782 -> 0 bytes data/fmri.webp | Bin 0 -> 50396 bytes 4 files changed, 4 insertions(+), 82 deletions(-) delete mode 100755 data/README.md delete mode 100644 data/fmri.gif create mode 100644 data/fmri.webp diff --git a/README.md b/README.md index 04e9c91..c1a98c4 100755 --- a/README.md +++ b/README.md @@ -15,6 +15,10 @@
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BrainLens: Image Reconstruction from Brain fMRI imaging using Diffusion Models

diff --git a/data/README.md b/data/README.md deleted file mode 100755 index 972d917..0000000 --- a/data/README.md +++ /dev/null @@ -1,82 +0,0 @@ - - - - -[![Contributors][contributors-shield]][contributors-url] -[![Forks][forks-shield]][forks-url] -[![Stargazers][stars-shield]][stars-url] -[![Issues][issues-shield]][issues-url] -[![MIT License][license-shield]][license-url] -[![LinkedIn][linkedin-shield]][linkedin-url] - - - - -
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- - - fmri - - -

BrainLens: Image Reconstruction from Brain fMRI imaging using Diffusion Models

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- - - Report Bug - · - Request Feature -

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- - - - - -## About The Project - -This project aims to reconstruct visual images from brain fMRI data using diffusion models. The project is inspired by the paper [Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors](https://arxiv.org/abs/2305.18274). - -The basic idea of the project is to train a model to reconstruct images from fMRI data. The model is split into two main pipelines, one to capute higher-level semantic features of the image from fMRI data, and the other one to capture the lower-level details of the image. The first pipeline is trained using contrastive learning to make the latent representation of the fMRI similar to the CLIP embedding of the image, and thus the model is able to capture the semantic features of the image. The second pipeline is trained using diffusion autoencoder models to capture the lower-level details of the image. The two pipelines are then combined to reconstruct the image from the fMRI data using Versatile Diffusion model. - -> **Note** -> This project is still under development and is not yet ready for deployment, but feel free to fork it and use it for your own purposes! - -## Data Source - -The dataset used in this project is from the Natural Scenes Dataset (NSD), which is a large-scale fMRI dataset conducted at ultra-high-field (7T) strength at the Center of Magnetic Resonance Research (CMRR) at the University of Minnesota. The dataset consists of whole-brain, high-resolution fMRI measurements of 8 healthy adult subjects while they viewed thousands of color natural scenes over the course of 30–40 scan sessions. While viewing these images, subjects were engaged in a continuous recognition task in which they reported whether they had seen each given image at any point in the experiment. [^1^] - -## Tech Stack - -The project utilizes the following technologies and libraries: - -- ![PyTorch](https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge&logo=PyTorch&logoColor=white) -- ![NumPy](https://img.shields.io/badge/NumPy-013243?style=for-the-badge&logo=NumPy&logoColor=white) -- ![Hugging Face](https://img.shields.io/badge/Hugging%20Face-FF4785?style=for-the-badge&logo=HuggingFace&logoColor=white) -- ![Diffusers](https://img.shields.io/badge/Diffusers-005571?style=for-the-badge&logo=Diffusers&logoColor=white) -- ![MLflow](https://img.shields.io/badge/MLflow-A10020?style=for-the-badge&logo=MLflow&logoColor=white) -- ![DVC](https://img.shields.io/badge/DVC-945DD6?style=for-the-badge&logo=Data%20Version%20Control&logoColor=white) - - -### References - -[^1^]: [Natural Scenes Dataset (NSD)](https://naturalscenesdataset.org/) - - - - -[contributors-shield]: https://img.shields.io/github/contributors/ahmed-alllam/BrainLens.svg?style=for-the-badge -[contributors-url]: https://github.com/ahmed-alllam/BrainLens/graphs/contributors -[forks-shield]: https://img.shields.io/github/forks/ahmed-alllam/BrainLens.svg?style=for-the-badge -[forks-url]: https://github.com/ahmed-alllam/BrainLens/network/members -[stars-shield]: https://img.shields.io/github/stars/ahmed-alllam/BrainLens.svg?style=for-the-badge -[stars-url]: https://github.com/ahmed-alllam/BrainLens/stargazers -[issues-shield]: https://img.shields.io/github/issues/ahmed-alllam/BrainLens.svg?style=for-the-badge -[issues-url]: https://github.com/ahmed-alllam/BrainLens/issues -[license-shield]: https://img.shields.io/github/license/ahmed-alllam/BrainLens.svg?style=for-the-badge -[license-url]: https://github.com/ahmed-alllam/BrainLens/blob/master/LICENSE.txt -[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555 -[linkedin-url]: https://linkedin.com/in/ahmed-e-allam -[product-screenshot]: images/screenshot.png - diff --git a/data/fmri.gif b/data/fmri.gif deleted file mode 100644 index 96c6b2c5509a86e39cc2b0cc6f439a70e081a4b0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1773782 zcmWifdq7g>|Hsb_INbE0sHmt%M8*5@Zf1GF8=4iCm6bI#YgSfNR(9PvfQn{?Wo36P zYj#;txw2O6fLd0zXtido?MIh2*V;x~+gxkz$Cv-$4>&x}^SQm=@28+JKVwVTpk`C z9uW}{85yZiD59dGqNAf@Vq#)rW8>oD;^X5J5)uf4NK8yjN=iyjPEJWlNli^nOG`^n zPtVB67&B%}W@hHtv17Bcvc`=YH-7y1?CflkB$Y~~N~Oxl$;r*l&CAQn&(AL?C{U}_ zg@uJhMMV=POeiico;Y#hq)C$|PoAvNXi7>-rc9YKb?Vg8($ZVkjs%rlH`3n{-`0&FItE;OQ zE?l^1(W1qR7uVF(ELpN->C&akmMzoibjz17U$J7v%9Sf?Yin1nTD5xh>NRWD)Ya9k zUAuPOx^?T@S(($Udz`0(K)M~-xMb{;)?^w_auAAkIDS6A2Z)ojZ5F_~MJZckenJj(hj+efi~=_wV0-@ZiB$Uw!rP;lr=L{(5L==+UD` zj~_q&=9_PxJbCi%x8HvE-FN@@fB)xnI-fp$`s~@W=g*(Nc=6)<@4tWf^5qXd{4hK` z{Ns;5zIyfQr=Na${rdIKKmYv8FTecy>#r`C>&=@tzy0>x@4x^4_U+q0{`lk1KmYve zufIk{M*jZ$?|1Ls{qxU1@87@w_uqd^m;mS~0HRX0)nYTC-qGa)){fprEUak1`t0F;`oH)^PX^8&xv>q;NgGt0 z>%6%`G4({Ldd1P(yNP+XVuw~7yR$E2WznhQ=RdxCC~YkiRMb6O(2>6*dd@8@>i)-? zotayTK0Wcp@#*Imt!i3z^6NABXZoNCyABVXtG<7(^!A0H)faWoy|Q91o}PYr_3!!p zCoi5U{hYe^o3Oa&2dCX+h?u+oE%E$DR8Y>r*eOZh-wukK_gzg%@(*`K;|#&ywsrq# zK0Nv4^`6Ur^glQ~v3KjZb!k7}y|8TA7oYr-^y_y!K7v1r-7xa(*>zFaJ@6YP?(HOXI=Jztalto=yc*x!cct<8q~JyeRS9C-@ZP&`-4%xW9{>JWyeZ@gKIT=7k&9)K}pTE zD9!#AU;cgUz>;Ujj~)E*x0%Nd&4U7tSw8T3e6(Y>Z12&-Gm>TeHt`9k}+*o6QG4{|byfz}yuL?zi0xt=Vty zO%&|!y{bI5ukT_>@xK1E%NLU%3nLgn>P-SSgk>(^hT=#ub3H83x*TLni4`|uLd?Jd z$!EJ(E*^U~@(0bhlMz3&JfzNZ1H|T*w*!bmd+s*zvEcXH#cNI_-QvVr$%j1B_wB+D zZvUCAsn{`n^%8R-0YaMl#peguR5=5B?XO9_jh~NQH99bD^C<>m$*m;BjY3QU3slcMD^KVq=#4@l9Z$lY!$K$NvF#elGp(yObNn>!6xIGYJQ)w(fYo z|K4fK>-0xkldHs8+LZgjVL|7?un>xYc|9AL2hxx^m+u|Bn04i4fuAnpm*gLR!T)@; z@=W_*N#N{$Z%9f0F*t4I2Il{Sa`P(Kzm5Rkc7`|rSXAmF5#d029Tau{$&ZKA&LIj4 z^w6k+-ya@#D@CJn8g9Z@eQoW-(cZ=k*e63n|1Da!sLwkX65|eMNvsYtQC?>WG&R2)umYU1BOpeTT|pNJSoq-QO_+;; ze`zGyT`ShgCIE2RMS!!P^eA`q%KJ3ziNga2x@-%_t_tuy_~(Oi=)Za7CKp-~kg!#0qx zco6|h2T68<+J+|KG%JOIfN8+s``r`0NRqW&3(MYGkgT%9aW1h)Gv;vIGyo7ywa{$s znpRdF$)z;)F@tH32HSR>Tl$C;VZI`@{! 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