#medical-imaging3 articles

3 articles

AINeutralarXiv – CS AI · 4h ago3

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When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion

Researchers conducted a systematic benchmark study on multimodal fusion between Electronic Health Records (EHR) and chest X-rays for clinical decision support, revealing when and how combining data modalities improves healthcare AI performance. The study found that multimodal fusion helps when data is complete but benefits degrade under realistic missing data scenarios, and released an open-source benchmarking toolkit for reproducible evaluation.

AIBullisharXiv – CS AI · 4h ago6

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MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening

Researchers developed MINT, a framework that transfers knowledge from MRI brain scans to speech analysis for early Alzheimer's detection. The system achieves comparable performance to speech-only methods while being grounded in neuroimaging biomarkers, enabling population-scale screening without requiring expensive MRI scans at inference.

AINeutralarXiv – CS AI · 4h ago0

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General vs Domain-Specific CNNs: Understanding Pretraining Effects on Brain MRI Tumor Classification

Research comparing CNN architectures for brain tumor classification found that general-purpose models like ConvNeXt-Tiny (93% accuracy) outperformed domain-specific medical pre-trained models like RadImageNet DenseNet121 (68% accuracy). The study suggests that contemporary general-purpose CNNs with diverse pre-training may be more effective for medical imaging tasks in data-scarce scenarios.