SphereFlow: Missing Modality Imputation via Geometric Transport on Hypersphere
SphereFlow addresses missing modality imputation by projecting all modalities into a shared hyperspherical latent space using a frozen self-supervised encoder, so cross-modal imputation becomes short-range transport on the sphere.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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SphereFlow addresses missing modality imputation by projecting all modalities into a shared hyperspherical latent space using a frozen self-supervised encoder, so cross-modal imputation becomes short-range transport on the sphere. It replaces the Gaussian noise source with the observed-modality latent so the velocity field learns only cross-modal displacement, and trains the decoder with a time-weighted loss covering the near-surface region the flow traverses. The authors prove a dimension-independent bound on worst-case off-manifold deviation governed by inter-modality similarity. Experiments on BraTS multi-modal MRI imputation and CT-MRI translation report state-of-the-art generation fidelity, and downstream tumor segmentation closes roughly eighty percent of the gap to the full-modality
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Source claims not independently checked; the evaluation is limited to BraTS MRI and CT-MRI translation, and the 'eighty percent of the gap' figure is the authors' reported result.
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Source claims not independently checked; the evaluation is limited to BraTS MRI and CT-MRI translation, and the 'eighty percent of the gap' figure is the authors' reported result.
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Replacing the Gaussian noise source with the observed-modality latent lets the velocity field learn only the cross-modal displacement.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseA time-weighted decoder loss extends the decoder's domain to the near-surface region traversed by the flow.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseThe paper proves worst-case off-manifold deviation drops to a dimension-independent bound set by inter-modality similarity.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseOn BraTS MRI imputation and CT-MRI translation, the method reports state-of-the-art generation fidelity across settings.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
Source claims not independently checked; the evaluation is limited to BraTS MRI and CT-MRI translation, and the 'eighty percent of the gap' figure is the authors' reported result.
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Why this may matter
Missing modalities are common in clinical imaging datasets. A method that imputes them with shorter geometric transport could improve downstream tasks such as tumor segmentation when some scans are absent.
Limits and open questions
Source claims not independently checked; the evaluation is limited to BraTS MRI and CT-MRI translation, and the 'eighty percent of the gap' figure is the authors' reported result.
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