用神经网络纠正MRI图像偏差，提升前列腺癌自动检测准确率
Hybrid Multi-Dimensional MRI Prostate Cancer Detection via Hadamard Network-Based Bias Correction and Residual Networks
- 分两阶段：先用哈达玛U-Net校正六种参数图的亮度不均，再用ResNet-18分类
- 在11×11像素块上融合二维与三维空间信息，提升检测一致性
- 比传统影像组学和基础卷积模型表现更好，适合临床辅助诊断
磁共振成像（MRI）对前列腺癌（PCa）诊断至关重要。尽管混合多维度MRI（HM-MRI）已提升诊断能力，仍亟需鲁棒的自动化人工智能（AI）检测方法。本文结合组织成分的定量HM-MRI与AI神经网络，提出基于哈达玛偏置网络与残差网络（HBR-Net-18）的两阶段框架。第一阶段采用哈达玛U-Net算法，校正通过物理引导自编码器（PIA）生成的六种参数化HM-MRI图中的强度不均（偏置场）。第二阶段使用ResNet-18进行局部块级分类，利用重叠的11×11像素块，融合2D层内与3D相邻层间信息以增强空间一致性。实验表明，该方法在敏感性与特异性间取得良好平衡，显著优于传统影像组学方法及基准卷积神经网络模型，展现了其临床应用潜力。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging (MRI) is vital for prostate cancer (PCa) diagnosis. While advanced techniques such as Hybrid Multi-dimensional MRI (HM-MRI) have enhanced diagnostic capabilities, the significant need remains for robust, automated Artificial Intelligence (AI)-based detection methods. In this study, we combine quantitative HM-MRI of tissue composition with an AI-based neural network. We propose the Hadamard-Bias Network plus ResNet18 (HBR-Net-18), a two-stage AI framework for PCa detection. In the first stage, a Hadamard U-Net-based algorithm suppresses intensity inhomogeneities (bias fields) across six parametric HM-MRI maps generated via a Physics-Informed Autoencoder (PIA). In the second stage, a Residual Network (ResNet-18) performs patch-level classification. The framework utilizes overlapping 11-by-11 patches, incorporating both 2D intra-slice and 3D inter-slice (adjacent-slice) information to improve spatial consistency. Our experimental results demonstrate that HB-Net achieves balanced sensitivity and specificity, significantly outperforming conventional radiomics-based approaches and baseline CNN models, highlighting its potential for clinical deployment.
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