用深度与人工特征结合的方法提升肝癌CT诊断准确率
Guiding the classification of hepatocellular carcinoma on 3D CT-scans using deep and handcrafted radiological features
- 借鉴LI-RADS标准，分两步融合深度与人工特征
- AUC提升6至18点，超越主流深度学习模型
- 临床验证效果媲美专家，适合辅助放射科医生
肝细胞癌是全球最常见的原发性肝癌（约占肝肿瘤的80%）。目前肝癌诊断金标准为肝穿刺活检，但临床实践中，放射科医生通常依据标准化的LI-RADS协议，通过解读肝脏CT图像进行视觉诊断，该协议包含五个影像学标准及相应决策树。本文提出一种自动预测经组织学确诊肝细胞癌的方法，旨在降低放射科医生间的诊断差异。我们首先发现，标准深度学习方法在具有挑战性的数据集上无法准确预测肝细胞癌，因此提出一种受LI-RADS启发的两阶段方法以提升性能。实验表明，该方法相比不同架构的深度学习基线，AUC提升6至18个百分点。此外，我们还进行了临床验证，结果表明本方法表现优于非专家放射科医生，且与专家水平相当。
原文摘要 · Abstract (English)
Hepatocellular carcinoma is the most spread primary liver cancer across the world ($\sim$80\% of the liver tumors). The gold standard for HCC diagnosis is liver biopsy. However, in the clinical routine, expert radiologists provide a visual diagnosis by interpreting hepatic CT-scans according to a standardized protocol, the LI-RADS, which uses five radiological criteria with an associated decision tree. In this paper, we propose an automatic approach to predict histology-proven HCC from CT images in order to reduce radiologists' inter-variability. We first show that standard deep learning methods fail to accurately predict HCC from CT-scans on a challenging database, and propose a two-step approach inspired by the LI-RADS system to improve the performance. We achieve improvements from 6 to 18 points of AUC with respect to deep learning baselines trained with different architectures. We also provide clinical validation of our method, achieving results that outperform non-expert radiologists and are on par with expert ones.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。