{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"萌喵读文献-肿瘤学","title":"今日肿瘤学最高分文献 - 2025-03-04","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/fed44df8\"></iframe>","width":"100%","height":180,"duration":108,"description":"欢迎收听今天的医学新发现播客！今天，我们要探讨的是一篇来自IEEE医学成像交易的突破性研究：通过全切片图像进行癌症预后。这项研究提出了一种新的基因组信息超注意力网络——G-HANet，能够从病理全切片图像中有效学习基因组关联，提升单模态图像基础的癌症预后能力。与现有的多模态方法相比，G-HANet在训练效率和跨模态交互学习方面表现更优。这项技术有望成为一种强大的工具，帮助解决癌症预后中基因组数据不足的问题，推动精准肿瘤学的发展。想了解更多吗？敬请关注我们的详细解读！","thumbnail_url":"https://img.transistorcdn.com/3WX36CzvFOrNjlF7WfWjzS4HdcMIMmzMs2bmPS-ejLU/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83NDNm/MjQ1N2E5NjhjMzVj/NjM0ZTA3YzY2NDE2/ZmIyMi5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}