TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model
We introduce TCAM-Diff, a novel 3D medical image generation model that reduces the memory requirements to encode and generate high-resolution 3D data. This model utilizes a decoder-only autoencoder method to learn triplane representation from dense volume and leverages generalization operations to prevent overfitting. Subsequently, it uses a triplane-aware cross-attention diffusion model to learn and integrate these features effectively. Furthermore, the features generated by the diffusion model can be rapidly transformed into 3D volumes using a pre-trained decoder module. Our experiments on t
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion ” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion ” ≈ “CompVis/stable-diffusion-v1-4””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion ” ≈ “stabilityai/stable-diffusion-3.5-large””
- LinkedLinked via arxiv author · 85%Zhenkai Zhang →
“TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model”
- LinkedLinked via arxiv author · 85%Krista A. Ehinger →
“TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model”
- LinkedLinked via arxiv author · 85%Tom Drummond →
“TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model”