Predicting genetic mutations from whole slide images is indispensable for
cancer diagnosis. However, existing work training multiple binary
classification models faces two challenges: (a) Training multiple binary
classifiers is inefficient and would inevitably lead to a class imbalance
problem. (b) The biological relationships among genes are overlooked, which
limits the prediction performance. To tackle these challenges, we innovatively
design a Biological-knowledge enhanced PathGenomic multi-label Transformer to
improve genetic mutation prediction performances. BPGT first establishes a
novel gene encoder that constructs gene priors by two carefully designed
modules: (a) A gene graph whose node features are the genes' linguistic
descriptions and the cancer phenotype, with edges modeled by genes' pathway
associations and mutation consistencies. (b) A knowledge association module
that fuses linguistic and biomedical knowledge into gene priors by
transformer-based graph representation learning, capturing the intrinsic
relationships between different genes' mutations. BPGT then designs a label
decoder that finally performs genetic mutation prediction by two tailored
modules: (a) A modality fusion module that firstly fuses the gene priors with
critical regions in WSIs and obtains gene-wise mutation logits. (b) A
comparative multi-label loss that emphasizes the inherent comparisons among
mutation status to enhance the discrimination capabilities. Sufficient
experiments on The Cancer Genome Atlas benchmark demonstrate that BPGT
outperforms the state-of-the-art.

从全幻灯图像中预测基因突变对于癌症诊断至关重要。本研究设计了一种生物知识增强的 PathGenomic 多标签 Transformer，通过构建基因优先级和基因突变预测模块，提高了预测性能，实验证明该方法优于现有最先进技术。