Researchers established a comprehensive benchmark for X-ray radiology report generation on the CheXpert Plus dataset, which was released without baseline implementations or evaluation results, and pro…
A new arXiv paper (2609.13481v1) presents a Hybrid Hierarchical CNN-LSTM Summarizer that frames biomedical and clinical summarization as extractive sentence selection rather than generation, eliminati…
Researchers Minjun Kim and Jong Hak Moon proposed Mr.Dec, a Transformer Decoder-based model that predicts 30-day hospital readmission by representing each admission as a time-aligned sequence of daily…
Researchers propose Mr.Dec, a multimodal Transformer Decoder that models hospital admissions as daily sequences of EHR updates and chest X-ray findings, achieving state-of-the-art performance on MIMIC…
Researchers introduced KANEx, the first framework leveraging Kolmogorov-Arnold Networks (KANs) to ground Vision-Language Model (VLM) reasoning for medical explainability, achieving a 10% improvement i…
A study from arXiv:2607.14205 found that federated learning (FL) on radiology reports is vulnerable to privacy leakage via gradient inversion, with exact sentence reconstruction rates ranging from 31%…
Researchers introduced CompDiff, a generative model that improves fairness in medical imaging by addressing demographic imbalances through a Hierarchical Conditioner Network. In tests on chest X-rays …