#交叉杂质
在缺乏标签试验数据的情况下,以深组织的定量选择对地形
深入的学习表明,在数量选择图象图图图图表中对组织进行量化的原子特征和功能参数(QOAT),但是由于缺少地面实况数据,对深入组织的适用受到阻碍。 我们建议DL-201C;QOAT-Net, 和编号x201D, 职能没有标签的试验数据:在培训后,通过无设计的×201C生成的数据对手进行配置系数; 模拟-业绩制和编号201D数据翻译。 在模拟、肺炎和维生动组织中,QOAT-Net提供了具有高空间解决办法的定性吸收图像。 这种做法使DL型QOAT和其他不可行应用在缺乏地面实况数据的情况下。
Deep learning-based quantitative optoacoustic tomography of deep tissues in the absence of labeled experimental data
Deep learning (DL) shows promise for quantitating anatomical features and functional parameters of tissues in quantitative optoacoustic tomography (QOAT), but its application to deep tissue is hindered by a lack of ground truth data. We propose DL-based “QOAT-Net,” which functions without labeled experimental data: a dual-path convolutional network estimates absorption coefficients after training with data-label pairs generated via unsupervised “simulation-to-experiment” data translation. In simulations, phantoms, and ex vivo and in vivo tissues, QOAT-Net affords quantitative absorption images with high spatial resolution. This approach makes DL-based QOAT and other imaging applications feasible in the absence of ground truth data.
Jiao Li, Cong Wang, Tingting Chen, Tong Lu, Shuai Li, Biao Sun, Feng Gao, Vasilis NtziachristosDeep learning (DL) shows promise for quantitating anatomical features and functional parameters of tissues in quantitative optoacoustic ... [Optica 9, 32-41 (2022)]
https://www.osapublishing.org/abstract.cfm?URI=optica-9-1-32