Title: HER2 Expression Prediction with Flexible Multi-Modal Inputs via Dynamic Bidirectional Reconstruction Show Chinese title

Title: 通过动态双向重建的灵活多模态输入进行HER2表达预测

Abstract: In breast cancer HER2 assessment, clinical evaluation relies on combined H&E and IHC images, yet acquiring both modalities is often hindered by clinical constraints and cost. We propose an adaptive bimodal prediction framework that flexibly supports single- or dual-modality inputs through two core innovations: a dynamic branch selector activating modality completion or joint inference based on input availability, and a cross-modal GAN (CM-GAN) enabling feature-space reconstruction of missing modalities. This design dramatically improves H&E-only accuracy from 71.44% to 94.25%, achieves 95.09% with full dual-modality inputs, and maintains 90.28% reliability under single-modality conditions. The "dual-modality preferred, single-modality compatible" architecture delivers near-dual-modality accuracy without mandatory synchronized acquisition, offering a cost-effective solution for resource-limited regions and significantly improving HER2 assessment accessibility. Show Chinese abstract

Abstract: 在乳腺癌HER2评估中，临床评估依赖于H&E和IHC图像的结合，但获取这两种模态通常受到临床限制和成本的阻碍。 我们提出了一种自适应双模态预测框架，通过两项核心创新灵活支持单模态或双模态输入：一种动态分支选择器，根据输入可用性激活模态补全或联合推理，以及一种跨模态生成对抗网络（CM-GAN），实现缺失模态的特征空间重建。 这种设计显著提高了仅H&E的准确性，从71.44%提高到94.25%，在完整双模态输入下达到95.09%，并在单模态条件下保持90.28%的可靠性。 “双模态优先，单模态兼容”的架构在无需强制同步采集的情况下实现了接近双模态的准确性，为资源有限地区提供了一种成本效益高的解决方案，并显著提高了HER2评估的可及性。