This paper introduces MedReCo and MedReCo-VLM, a framework that enables entity-aware cross-image reasoning for medical imaging, allowing AI to compare current scans with prior studies and analogous ca…

This paper introduces ClinFusion, a vision-centric multimodal large language model designed for holistic medical understanding, featuring a Cascade Spatial-Aware Locality Fusion operator and a vision-…

The paper introduces AutoMedBench, a novel workflow-aware benchmark that evaluates autonomous medical-AI agents across a five-stage research process, revealing that agents struggle most with validatio…

CodeCytos is a novel coding-based reasoning agent framework that enables dynamic, programmable interaction with spatial molecular imaging data, significantly improving the automation and customization…

The paper introduces a novel framework that aligns single white blood cell images with genetic data (karyotype and somatic mutations) to significantly improve the diagnosis of blood cancers, outperfor…

The paper addresses 'Template Collapse' in 3D CT report generation—where models generate generic reports—by proposing CLarGen, a decoupled framework that significantly improves clinical accuracy and d…

This paper introduces MMed-Bench-IR, a benchmark for multilingual medical retrieval in clinical settings, evaluating cross-lingual alignment, concept discrimination, and evidence retrieval.

The paper introduces AMNESIA, the first large-scale, open-source benchmark for medical unlearning, demonstrating that current unlearning methods struggle to separate individual patient data from share…

This paper proposes an anatomically aware frequency-and-structure-guided vision transformer (AA-ViT) for accurate and non-invasive contrast enhanced MRI (CEMRI) synthesis using pre-contrast MRI modali…

This paper critically reviews the intersection of philosophy of science and explainable AI (XAI) in medicine, identifying necessary conditions for a philosophically grounded approach to explanation.

This paper introduces GLORIA, a novel framework for aligning and fusing histopathology, mRNA expression, and magnetic resonance imaging data for glioma grading and survival prediction.

The paper introduces MedPMC, a framework that transforms permissively licensed literature into high-fidelity infrastructure for medical multimodal models, resulting in improved performance on various…

This paper introduces TRACE, a model for interpretable 4-class glioblastoma response classification on longitudinal 3D MRI using a structured concept reasoning approach.

The paper proposes a novel Global Context-aware Squeeze and Excite Residual UNet (GCSER-UNet) network, which significantly enhances brain tumor segmentation accuracy on benchmark MRI datasets.