ResUnet++融合残差结构与人机交互，提升脑瘤定位精度。
Residual Connection Networks in Medical Image Processing: Exploration of ResUnet++ Model Driven by Human Computer Interaction
- 在上下采样中嵌入残差块，保留关键图像特征
- 在LGG数据集上实现98.17%的交并比损失
- 支持实时反馈，适合临床医生交互使用
由于肿瘤异质性和结构复杂性，从医学图像中准确识别和定位脑瘤仍具挑战。卷积神经网络（CNN），特别是ResNet和Unet，在医学图像处理中取得了显著进展，具备强大的图像分割能力。然而，关于其与人机交互（HCI）结合以提升可用性、可解释性和临床适用性的研究仍有限。本文提出ResUnet++，一种融合ResNet与Unet++的先进混合模型，旨在提升肿瘤检测与定位性能，并促进临床医生与影像系统间的无缝交互。ResUnet++在下采样与上采样阶段均引入残差块，确保关键图像特征得以保留。通过融入HCI原则，该模型提供直观的实时反馈，使临床医生能有效可视化并交互查看肿瘤定位结果，促进决策判断，提升临床工作流程效率。在LGG Segmentation Dataset上的评估显示，其交并比损失达98.17%。结果表明，该模型具有优异的分割性能，具备实际应用潜力。通过连接先进医学影像技术与人机交互，ResUnet++为开发交互式诊断工具奠定了基础，有望增强临床信任、提高决策准确性与患者预后，推动AI在医疗工作流中的深度融合。
原文摘要 · Abstract (English)
Accurate identification and localisation of brain tumours from medical images remain challenging due to tumour variability and structural complexity. Convolutional Neural Networks (CNNs), particularly ResNet and Unet, have made significant progress in medical image processing, offering robust capabilities for image segmentation. However, limited research has explored their integration with human-computer interaction (HCI) to enhance usability, interpretability, and clinical applicability. This paper introduces ResUnet++, an advanced hybrid model combining ResNet and Unet++, designed to improve tumour detection and localisation while fostering seamless interaction between clinicians and medical imaging systems. ResUnet++ integrates residual blocks in both the downsampling and upsampling phases, ensuring critical image features are preserved. By incorporating HCI principles, the model provides intuitive, real-time feedback, enabling clinicians to visualise and interact with tumour localisation results effectively. This fosters informed decision-making and supports workflow efficiency in clinical settings. We evaluated ResUnet++ on the LGG Segmentation Dataset, achieving a Jaccard Loss of 98.17%. The results demonstrate its strong segmentation performance and potential for real-world applications. By bridging advanced medical imaging techniques with HCI, ResUnet++ offers a foundation for developing interactive diagnostic tools, improving clinician trust, decision accuracy, and patient outcomes, and advancing the integration of AI in healthcare workflows.
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