Here are the links to the repositories of the recently featured studies. 😃
The MoME approach adapts AI models to diverse clinical expertise across centers without data sharing.
We introduce LLMSeg, the first LLM-driven AI integrating text and imaging for superior target volume contouring in breast cancer therapy.
We present DISTL, a self-supervision and self-training framework that enhances chest x-ray diagnosis, outperforming fully supervised models in real-world settings.
We propose a patch-based CNN with fewer parameters for COVID-19 diagnosis using chest X-rays, achieving state-of-the-art performance with clinically interpretable saliency maps.
