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A benchmark for evaluating whether vision-language models genuinely ground their diagnostic statements in visual evidence during chest X-ray report generation, …
A missing-aware multimodal framework that fuses CT, Whole-Slide Histopathology, and clinical tabular data for survival prediction in unresectable stage II–III N…
A novel Multi-Dataset Multi-Task (MDMT) learning framework that predicts COVID-19 prognostic outcomes from chest X-rays by jointly training on two publicly avai…
The first systematic benchmark of fine-tuning strategies applied to CNNs and Foundation Models for COVID-19 prognosis prediction from chest X-rays, under realis…
This is a code implemention of the framework proposed in the paper "Multimodal Clinical Data Integration for Prognosis of Pulmonary Embolism: A Comparative Stud…