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Università Campus Bio-Medico di Roma, Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Universitetstorget
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Self-PreTraining boosts transformer accuracy in medical time series by up to 6 percentage points, even with limited data.
High report quality in VLMs can mask a dangerous reliance on visual shortcuts, revealing a critical flaw in how we evaluate radiology report generation.
A new multimodal deep learning model accurately predicts cancer treatment response from incomplete patient data, even when key clinical information is missing.
Quantum-enhanced neural networks can forecast patient vitals with accuracy rivalling classical methods, while showing greater resilience to noisy or incomplete data.
Injecting retrieved anatomical priors into text-to-CT generation dramatically improves image fidelity and clinical consistency, offering a scalable path to more realistic medical image synthesis.
Radiology report generation can be both more accurate AND more interpretable: CEMRAG uses visual concepts to enhance multimodal RAG, challenging the assumed trade-off between transparency and diagnostic accuracy.