.png){kind=icon}
返回
Knowledge-guided multi-modality transformer for multi-label genetic mutation prediction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2026.113047.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
• The first work to propose a multi-label framework for predicting genetic mutations from whole slide images (WSIs). • Intrinsic biological knowledge is incorporated to capture gene dependencies, enabling the model to reason about mutations in a multi-label context. • A graph-based gene encoder is proposed to capture linguistic, phenotypic, and pathway-level associations. • A label decoder is proposed to align gene priors with spatial WSI features and employs a comparative multi-label loss to improve discrimination across mutation statuses. • The framework improves accuracy and interpretability, and is validated on 9 TCGA cancer types.
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Artificial intelligence to identify genetic alterations in conventional histopathology人工智能识别常规组织病理学中的遗传改变.png)
JOURNAL OF PATHOLOGY
OAAI
JOURNAL OF PATHOLOGYIF5.2
OAAI分享收藏分享收藏Data-efficient and weakly supervised computational pathology on whole-slide images全载玻片图像上的数据高效和弱监督计算病理学OAAI
OAAI分享收藏Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics dataPathformer: 使用多组学数据进行疾病诊断和预后的生物通路知情转换器
OAAI
OAAI分享收藏Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden对100,000人类癌症基因组的分析揭示了肿瘤突变负担的景观GENOME MEDICINEOAAI
GENOME MEDICINEIF11.2
OAAI分享收藏Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis泛癌症计算组织病理学揭示突变，肿瘤组成和预后NATURE CANCEROAAI
NATURE CANCERIF28.5
OAAI分享收藏Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathologyOAAI
OAAI分享收藏
查看更多内容