数据与计算发展前沿 ›› 2019, Vol. 1 ›› Issue (2): 37-52.
doi: 10.11871/jfdc.issn.2096-742X.2019.02.004
所属专题: “人工智能”专刊
收稿日期:
2019-09-25
出版日期:
2019-12-20
发布日期:
2020-01-15
通讯作者:
马杰超
作者简介:
俞益洲,1970年生,深睿医疗首席科学家,曾任美国伊利诺依大学香槟分校终身教授。加州大学伯克利分校计算机博士,美国计算机协会杰出科学家,IEEE Fellow。已在具有影响力的国际会议和期刊发表学术论文150余篇,多次获最佳论文奖。担任图像、视觉计算领域重要国际会议程序委员会委员或主席,并在多个国际学术期刊担任副主编。基金资助:
Yu Yizhou,Ma Jiechao*(),Shi Dejun,Zhou Zhen
Received:
2019-09-25
Online:
2019-12-20
Published:
2020-01-15
Contact:
Ma Jiechao
摘要:
【目标】综述近年来深度学习(Deep Learning, DL)在医学影像分析领域的研究和应用进展。【文献范围】采用关键词检索和引文二次检索的方法初步收集相关论文。【方法】首先简要介绍基于卷积神经网络的DL模型,然后按病症介绍近年来DL在医学影像辅助诊断中的表现,病症包括脑卒中、肺结节、骨龄测量等。【结果】DL在多种疾病的影像辅助诊断中展现出优势,包括精度高、速度快、结果稳定、可规模化等。同时,很多问题阻碍了DL从实验走向临床,如依赖大量数据、标注标准不统一、模型泛化能力欠佳、可解释性不足等。【局限】检索文献仅覆盖最近几年的工作,对于更久之前的可能存在遗漏。【结论】深度学习可提高放射医师解读影像的效率和精度,但DL还不完美,在广泛渗透医学影像解读之前,还需要经历长时间的研究和验证。
俞益洲, 马杰超, 石德君, 周振. 深度学习在医学影像分析中的应用综述[J]. 数据与计算发展前沿, 2019, 1(2): 37-52.
Yu Yizhou, Ma Jiechao, Shi Dejun, Zhou Zhen. Application of Deep Learning in Medical Imaging Analysis: A Survey[J]. Frontiers of Data and Computing, 2019, 1(2): 37-52.
表1
在多种医学影像分析中DL与传统方法和人类的表现对比"
任务和评价指标 | 数据模态 | DL | 人工特征方法 | 人类/专家 | DL+人类 | 数据集 |
---|---|---|---|---|---|---|
脑肿瘤分割/Dice | 核磁 | 88.0%[ | 79.0%[ | - | - | 2013 BRATS |
转移乳腺癌检测/准确度[ | 病理切片 | 92.5% | - | 96.60% | 99.50% | Camelyon16 |
视网膜血管分割/准确度 | 眼底图片 | 96.0%~97.3%[ | 92.7~94.5% | 94.70%[ | - | DRIVE/STARE |
糖网筛查/AUC | 眼底图片 | 99.0%[ | 87.8%[ | - | - | Kaggle’s dataset |
肺结节筛查/敏感度 | CT | 95.00%[ | 63.20%[ | - | - | LUNA16 |
肝分割/体积重叠误差[ | CT | 5.37% | 7.73% | - | - | SLIVER07 |
皮肤癌分类/准确度[ | 皮肤图片 | 72.10% | - | 66.00% | - | Public+Private |
乳腺良恶识别/敏感性[ | DBT* | 93.00% | 85.20% | - | - | Private |
肝肿瘤分割/Dice[ | CT | 80.1% | 75.67%~79.78% | - | - | Private |
肠息肉筛查/检出率[ | 结肠镜 | 96.40% | - | 7%~53% | - | Private |
*DBT:数字乳腺断层合成显像 |
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