biomedia-mira/causal-gen
(ICML 2023) High Fidelity Image Counterfactuals with Probabilistic Causal Models
This project helps medical professionals and researchers understand how changes to underlying health factors might alter medical images, like X-rays or MRI scans. You input an existing medical image and specify a hypothetical change to a condition, and it outputs a new image showing what that change might look like. This is useful for scientists exploring disease progression or clinicians planning interventions, allowing them to visualize 'what-if' scenarios.
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Use this if you need to generate high-fidelity counterfactual medical images to explore the impact of specific causal factors on visual medical data.
Not ideal if you are looking to diagnose conditions directly from images or require real-time image generation for clinical decision-making.
Stars
67
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10
Language
Python
License
MIT
Category
Last pushed
Mar 24, 2025
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