Halfway to 3D: Ensembling 2.5D and 3D Models for Robust COVID-19 CT Diagnosis | Signal Canvas | ScienceToStartup

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Halfway to 3D: Ensembling 2.5D and 3D Models for Robust COVID-19 CT Diagnosis

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Proof freshness: stale
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Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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Halfway to 3D: Ensembling 2.5D and 3D Models for Robust COVID-19 CT Diagnosis

Canonical ID halfway-to-3d-ensembling-2-5d-and-3d-models-for-robust-covid-19-ct-diagnosis | Route /signal-canvas/halfway-to-3d-ensembling-2-5d-and-3d-models-for-robust-covid-19-ct-diagnosis

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/halfway-to-3d-ensembling-2-5d-and-3d-models-for-robust-covid-19-ct-diagnosis

MCP example

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    "query_text": "Summarize Halfway to 3D: Ensembling 2.5D and 3D Models for Robust COVID-19 CT Diagnosis"
  }
}

source_context

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
for binary COVID-19 detection, the ensemble achieves 94.48% accuracy and a 0.9426 Macro F1-score
Implicationpartial
Directly stated in abstract with specific numeric results
Verificationpartial
partial
Evidencepartial
for multi-class disease classification the 2.5D DINOv3 model achieves the best performance with 79.35% accuracy and a 0.7497 Macro F1-score
Implicationpartial
Directly stated in abstract with specific performance metrics
Verificationpartial
partial
Evidencepartial
the 3D branch employs a ResNet-18 architecture to model volumetric context and is pretrained with Variance Risk Extrapolation (VREx) followed by supervised contrastive learning to improve cross-source robustness
Implicationpartial
Directly stated in abstract with technical implementation details
Verificationpartial
partial
Evidencepartial
integrates both 2.5D and 3D representations to capture complementary slice-level and volumetric information
Implicationpartial
Directly stated in abstract as the core methodological approach
Verificationpartial
partial
Evidencepartial
outperforming both individual models
Implicationpartial
Directly stated in abstract with comparative performance claim
Verificationpartial
partial
Evidencepartial
The 2.5D branch processes multi-view CT slices (axial, coronal, sagittal) using a DINOv3 vision transformer
Implicationpartial
Directly stated in abstract with specific technical implementation
Verificationpartial
partial
Evidencepartial
Model performance may degrade on CT scans from new manufacturers not in training data
Implicationpartial
Stated in analysis section as a risk, but not directly tested or quantified in the paper
Verificationpartial
partial
Evidencepartial
Requires large, diverse CT datasets for training to maintain accuracy across new hospital sources
Implicationpartial
Stated in analysis section as a caveat, implied by the cross-source robustness focus
Verificationpartial
partial