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A specific methodology leverages generative models to transform medical images from one modality or characteristic to another without relying on paired training data. This approach aims to synthesize images that resemble a target domain, given an input image from a source domain, even when corresponding images in both domains are unavailable for direct comparison during the learning process. For instance, one can generate a synthetic Computed Tomography (CT) scan from a Magnetic Resonance Imaging (MRI) scan of the same patient’s brain, despite lacking paired MRI-CT datasets.

This technique addresses a critical challenge in medical imaging: the scarcity of aligned, multi-modal datasets. Obtaining paired images can be expensive, time-consuming, or ethically problematic due to patient privacy and radiation exposure. By removing the need for paired data, this approach opens possibilities for creating large, diverse datasets for training diagnostic algorithms. It also facilitates cross-modality analysis, enabling clinicians to visualize anatomical structures and pathological features that might be more apparent in one modality than another. Historically, image translation methods relied on supervised learning with paired data, which limited their applicability in many clinical scenarios.

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