用轻量级网络降低前列腺癌磁共振误诊率
RadHop-Net: A Lightweight Radiomics-to-Error Regression for False Positive Reduction In MRI Prostate Cancer Detection
- 通过放射组学提取可疑区域，再用新模型预测误差进行修正
- 在pi-cai数据集上平均精度从0.407提升至0.468
- 模型小且高效，适合临床部署和实时筛查
临床显著性前列腺癌（csPCa）是男性癌症死亡的主要原因，但早期诊断后生存率很高。双参数磁共振成像（bpMRI）已成为csPCa的主流筛查手段，但存在高误诊率，增加诊断成本与患者不适。本文提出RadHop-Net，一种用于减少误诊的轻量级卷积神经网络。该方法分为两阶段：第一阶段基于数据驱动的放射组学提取候选病灶区域；第二阶段利用RadHop-Net扩大每个区域的感受野，以补偿第一阶段的预测误差。此外，设计了一种新型回归损失函数，平衡误诊（FP）与真阳性（TP）的影响。RadHop-Net采用放射组学到误差的训练方式，摆脱了传统的体素到标签的范式。在公开数据集pi-cai上，第二阶段将病灶检测的平均精度（AP）从0.407提升至0.468，同时保持显著更小的模型规模。
原文摘要 · Abstract (English)
Clinically significant prostate cancer (csPCa) is a leading cause of cancer death in men, yet it has a high survival rate if diagnosed early. Bi-parametric MRI (bpMRI) reading has become a prominent screening test for csPCa. However, this process has a high false positive (FP) rate, incurring higher diagnostic costs and patient discomfort. This paper introduces RadHop-Net, a novel and lightweight CNN for FP reduction. The pipeline consists of two stages: Stage 1 employs data driven radiomics to extract candidate ROIs. In contrast, Stage 2 expands the receptive field about each ROI using RadHop-Net to compensate for the predicted error from Stage 1. Moreover, a novel loss function for regression problems is introduced to balance the influence between FPs and true positives (TPs). RadHop-Net is trained in a radiomics-to-error manner, thus decoupling from the common voxel-to-label approach. The proposed Stage 2 improves the average precision (AP) in lesion detection from 0.407 to 0.468 in the publicly available pi-cai dataset, also maintaining a significantly smaller model size than the state-of-the-art.
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