准确评估脑肿瘤在胶质瘤的诊断和治疗中起着至关重要的作用。虽然卷积神经网络（CNN）在这一任务中表现出了很大的潜力，但其临床适用性仍受到模型的可解释性和鲁棒性的限制。在传统框架中，首先训练分类模型，然后生成可视化解释。然而，这种方法往往会导致模型更加注重分类性能或复杂性，使得难以实现精确的可视化解释。受到这些挑战的启发，我们提出了统一可视化和分类网络（UniVisNet），这是一个新颖的框架，旨在提高分类性能和生成高分辨率可视化解释。UniVisNet通过引入基于子区域的注意力机制来解决注意力不对齐问题，取代了传统的下采样操作。此外，多尺度特征图被融合以实现更高的分辨率，从而生成详细的可视化解释。为了简化流程，我们引入了统一可视化和分类头（UniVisHead），它直接生成可视化解释，无需额外的分离步骤。通过大量的实验，我们提出的UniVisNet始终优于强基线分类模型和常见的可视化方法。值得注意的是，UniVisNet在胶质瘤分级任务上取得了显著的成果，包括94.7%的AUC，89.3%的准确率，90.4%的敏感度和85.3%的特异度。此外，UniVisNet提供了超越现有方法的可视化解释。总之，UniVisNet通过同时提高分类性能和生成高分辨率可视化解释，在脑肿瘤分级中创新地生成可视化解释。这项工作为深度学习的临床应用做出了贡献，赋予临床医生对胶质瘤的空间异质性有全面的认识。 版权 © 2023 Elsevier Ltd. 保留所有权利。

Accurate grading of brain tumors plays a crucial role in the diagnosis and treatment of glioma. While convolutional neural networks (CNNs) have shown promising performance in this task, their clinical applicability is still constrained by the interpretability and robustness of the models. In the conventional framework, the classification model is trained first, and then visual explanations are generated. However, this approach often leads to models that prioritize classification performance or complexity, making it difficult to achieve a precise visual explanation. Motivated by these challenges, we propose the Unified Visualization and Classification Network (UniVisNet), a novel framework that aims to improve both the classification performance and the generation of high-resolution visual explanations. UniVisNet addresses attention misalignment by introducing a subregion-based attention mechanism, which replaces traditional down-sampling operations. Additionally, multiscale feature maps are fused to achieve higher resolution, enabling the generation of detailed visual explanations. To streamline the process, we introduce the Unified Visualization and Classification head (UniVisHead), which directly generates visual explanations without the need for additional separation steps. Through extensive experiments, our proposed UniVisNet consistently outperforms strong baseline classification models and prevalent visualization methods. Notably, UniVisNet achieves remarkable results on the glioma grading task, including an AUC of 94.7%, an accuracy of 89.3%, a sensitivity of 90.4%, and a specificity of 85.3%. Moreover, UniVisNet provides visually interpretable explanations that surpass existing approaches. In conclusion, UniVisNet innovatively generates visual explanations in brain tumor grading by simultaneously improving the classification performance and generating high-resolution visual explanations. This work contributes to the clinical application of deep learning, empowering clinicians with comprehensive insights into the spatial heterogeneity of glioma.Copyright © 2023 Elsevier Ltd. All rights reserved.