Liu Xiang, Liang James, Zhang Jianwei, Qian Zihan, Xing Phoebe, Chen Taige, Yang Shanchieh, Chukwudi Chijioke, Qiu Liang, Liu Dongfang, Zhao Junhan
Alvus Health Inc., Harvard Pagliuca Life Lab, USA; Department of Biostatistics & Health Data Science, Indiana University, USA.
Department of Computer Engineering, Rochester Institute of Technology, USA.
Comput Methods Programs Biomed. 2025 Jun;265:108705. doi: 10.1016/j.cmpb.2025.108705. Epub 2025 Mar 13.
Hierarchical neural networks are pivotal in medical imaging for multi-scale representation, aiding in tasks such as object detection and segmentation. However, their effectiveness is often limited by the loss of intra-scale information and misalignment of inter-scale features. Our study introduces the Integrated-Scale Pyramidal Interactive Reconfiguration to Enhance feature learning (INSPIRE).
INSPIRE focuses on intra-scale semantic enhancement and precise inter-scale spatial alignment, integrated with a novel spatial-semantic back augmentation technique. We evaluated INSPIRE's efficacy using standard hierarchical neural networks, such as UNet and FPN, across multiple medical segmentation challenges including brain tumors and polyps. Additionally, we extended our evaluation to object detection and semantic segmentation in natural images to assess generalizability.
INSPIRE demonstrated superior performance over standard baselines in medical segmentation tasks, showing significant improvements in feature learning and alignment. In identifying brain tumors and polyps, INSPIRE achieved enhanced precision, sensitivity, and specificity compared to traditional models. Further testing in natural images confirmed the adaptability and robustness of our approach.
INSPIRE effectively enriches semantic clarity and aligns multi-scale features, achieving integrated spatial-semantic coherence. This method seamlessly integrates with existing frameworks used in medical image analysis, thereby promising to significantly enhance the efficacy of computer-aided diagnostics and clinical interventions. Its application could lead to more accurate and efficient imaging processes, essential for improved patient outcomes.
分层神经网络在医学成像中对于多尺度表示至关重要，有助于诸如目标检测和分割等任务。然而，它们的有效性常常受到尺度内信息丢失和尺度间特征不对准的限制。我们的研究引入了用于增强特征学习的集成尺度金字塔交互式重构（INSPIRE）。
INSPIRE专注于尺度内语义增强和精确的尺度间空间对准，并集成了一种新颖的空间语义反向增强技术。我们使用标准的分层神经网络，如UNet和FPN，在包括脑肿瘤和息肉在内的多个医学分割挑战中评估了INSPIRE的功效。此外，我们将评估扩展到自然图像中的目标检测和语义分割，以评估其通用性。
INSPIRE在医学分割任务中表现出优于标准基线的性能，在特征学习和对准方面有显著改进。在识别脑肿瘤和息肉方面，与传统模型相比，INSPIRE实现了更高的精度、灵敏度和特异性。在自然图像中的进一步测试证实了我们方法的适应性和鲁棒性。
INSPIRE有效地丰富了语义清晰度并对准了多尺度特征，实现了集成的空间语义一致性。该方法与医学图像分析中使用的现有框架无缝集成，从而有望显著提高计算机辅助诊断和临床干预的功效。其应用可能导致更准确、高效的成像过程，这对于改善患者预后至关重要。
