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摘要:
作为人工智能（artificial intelligence，AI）的主流，深度学习在计算机视觉、图像多尺度特征提取领域已有所进展。2016年以来，深度学习方法在计算机断层成像（从积分特性，如线积分，实现内部结构的图像重建）方面也取得了进步。总体而言，在人工智能领域，尤其是基于人工智能的成像领域，令人兴奋的前景和挑战并存，包括准确性、鲁棒性、泛化性、可解释性等一系列问题。基于2021年8月2日SPIE Optics+Photonics上的大会邀请报告，本文介绍X射线成像和深度学习的背景，低剂量CT、稀疏数据CT、深度影像组学的代表性成果，讨论对于X射线CT、其他成像模式以及多模态成像而言，数据驱动和模型驱动方法融合带来的机会，以期显著促进精准医疗的进步。
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图 1 深度学习的基本思想。神经网络由相互连接的神经元组成，每个神经元先执行线性计算 （内积），再执行非线性运算（阈值化，如ReLU）。神经元之间的权重参数被随机初始化，并通过使用训练数据迭代更新以期损失函数最小化（例如上图迭代中显示为“4”的绝对误差），最终输出正确答案（在本例中为“5”）
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图 3 使用TITLE-ABS-KEY规则=（deep learning AND medical AND image AND reconstruct*）AND（X-ray OR CT OR computed tomography）在Scopus进行检索的可视化结果
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图 6 利用深度学习对如蛋白质分子的非晶体目标进行基于AlphaFold的X射线断层成像。由于超高分辨率所需的强辐射会立即摧毁目标，因此对于断层成像重建而言，只能采集到单次发射的散射波前数据，这是极具挑战性的极稀疏数据成像任务
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图 7 对抗性攻击使训练好的深度网络处于高度危险之中。虽然原始数字图像（左）被深度分类器正确分类，但是我们能设计一个微小的对抗噪声（中）并添加到原始图像中，形成视觉上差不多的图像（右），让该图像被同一分类器错误分类
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