HER2 Expression Prediction with Flexible Multi-Modal Inputs via Dynamic Bidirectional Reconstruction

• URL: http://arxiv.org/abs/2506.10006v2
• Date: Thu, 31 Jul 2025 07:57:18 GMT
• Title: HER2 Expression Prediction with Flexible Multi-Modal Inputs via Dynamic Bidirectional Reconstruction
• Authors: Jie Qin, Wei Yang, Yan Su, Yiran Zhu, Weizhen Li, Yunyue Pan, Chengchang Pan, Honggang Qi,
• Abstract summary: We propose an adaptive bimodal prediction framework that flexibly supports single- or dual-modality inputs.<n>Design dramatically improves H&E-only accuracy from 71.44% to 94.25%, 95.09% with full dual-modality inputs, and maintains 90.28% reliability under single-modality conditions.
• Score: 25.739068829471297
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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