Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II
Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II
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医学图像计算和计算机辅助干预 - MICCAI 2018 - 第 21 届国际会议，西班牙格拉纳达，2018 年 9 月 16-20 日，会议记录，第二部分
DOI：
10.1007/978-3-030-00934-2_37
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Gu Y
中科院分区：
文献类型：
--
作者：
Gu Y
本文提出了一种用于基于探针的共焦激光内窥镜（pCLE）的弱监督表示学习框架。与之前基于帧和基于马赛克的方法不同，该框架采用深度卷积神经网络，并将基于帧的特征学习、全局诊断预测和局部肿瘤检测集成到统一的端到端模型中。 pCLE 马赛克中的潜在对象是通过语义标签传播推断的，并且深度卷积神经网络使用复合损失函数进行训练。对 700 个 pCLE 样本的实验表明，仅用全局监督训练的所提出的方法能够在全局和局部诊断预测上实现更高的准确性。
This paper proposes a weakly-supervised representation learning framework for probe-based confocal laser endomicroscopy (pCLE). Unlike previous frame-based and mosaic-based methods, the proposed framework adopts deep convolutional neural networks and integrates frame-based feature learning, global diagnosis prediction and local tumor detection into a unified end-to-end model. The latent objects in pCLE mosaics are inferred via semantic label propagation and the deep convolutional neural networks are trained with a composite loss function. Experiments on 700 pCLE samples demonstrate that the proposed method trained with only global supervisions is able to achieve higher accuracy on global and local diagnosis prediction.