准确预测克尔斯滕大鼠肉瘤（KRAS）突变状态对于晚期结直肠癌患者的个体化治疗至关重要。然而，尽管深度学习模型在某些方面表现出色，但它们往往忽视了多个任务之间的协同促进以及对全局和局部信息的考虑，这会显着降低预测精度。为了解决这些问题，本文提出了一种称为多任务全局局部协作混合网络（CHNet）的创新方法，旨在更准确地预测患者的 KRAS 突变状态。 CHNet 由两个分支组成，可以分别从分割和分类任务中提取全局和局部特征，并交换互补信息以协作执行这些任务。在这两个分支中，我们设计了通道混合变压器（CHT）和空间混合变压器（SHT）。这些 Transformer 集成了 Transformer 和 CNN 的优点，采用级联混合注意力和卷积从两个任务中捕获全局和局部信息。此外，我们创建了自适应协作注意（ACA）模块，以通过指导促进分割和分类特征的协作融合。此外，我们引入了一种新颖的类激活图（CAM）损失，以鼓励 CHNet 学习两个任务之间的补充信息。我们在 T2 加权 MRI 数据集上评估 CHNet，KRAS 突变状态预测的准确率达到 88.93%，优于代表性 KRAS 突变状态预测方法的性能。结果表明，我们的 CHNet 通过多任务协作促进并考虑全局-局部信息方式，可以更准确地预测患者的 KRAS 突变状态，这可以帮助医生为患者制定更个性化的治疗策略。版权所有 © 2024。由 Elsevier 出版B.V.

Accurate prediction of Kirsten rat sarcoma (KRAS) mutation status is crucial for personalized treatment of advanced colorectal cancer patients. However, despite the excellent performance of deep learning models in certain aspects, they often overlook the synergistic promotion among multiple tasks and the consideration of both global and local information, which can significantly reduce prediction accuracy. To address these issues, this paper proposes an innovative method called the Multi-task Global-Local Collaborative Hybrid Network (CHNet) aimed at more accurately predicting patients' KRAS mutation status. CHNet consists of two branches that can extract global and local features from segmentation and classification tasks, respectively, and exchange complementary information to collaborate in executing these tasks. Within the two branches, we have designed a Channel-wise Hybrid Transformer (CHT) and a Spatial-wise Hybrid Transformer (SHT). These transformers integrate the advantages of both Transformer and CNN, employing cascaded hybrid attention and convolution to capture global and local information from the two tasks. Additionally, we have created an Adaptive Collaborative Attention (ACA) module to facilitate the collaborative fusion of segmentation and classification features through guidance. Furthermore, we introduce a novel Class Activation Map (CAM) loss to encourage CHNet to learn complementary information between the two tasks. We evaluate CHNet on the T2-weighted MRI dataset, and achieve an accuracy of 88.93% in KRAS mutation status prediction, which outperforms the performance of representative KRAS mutation status prediction methods. The results suggest that our CHNet can more accurately predict KRAS mutation status in patients via a multi-task collaborative facilitation and considering global-local information way, which can assist doctors in formulating more personalized treatment strategies for patients.Copyright © 2024. Published by Elsevier B.V.