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CLAIR-MAE is a novel multimodal framework that integrates head computed tomography (CT) imaging with clinical patient information through cross-attention mechanisms and contrastive learning for early neurological outcome prediction after cardiac arrest.

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CLAIR - Contrastive Language and Image Reasoning with Masked Auto Encoders

This repository contains the official implementation of CLAIR: A clinical decision-support tool using multimodal AI for early outcome prediction in post–cardiac arrest patients

Kindly email us at akasturi@ur.rochester.edu for training and other supporting scripts.

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CLAIR-MAE is a novel multimodal framework that integrates head computed tomography (CT) imaging with clinical patient information through cross-attention mechanisms and contrastive learning for early neurological outcome prediction after cardiac arrest.

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