While the use of artificial intelligence (AI) for medical image analysis is gaining wide acceptance, the expertise, time and cost required to generate annotated data in the medical field are significantly high, due to limited availability of both data and expert annotation. Strongly supervised object localization models require data that is exhaustively annotated, meaning all objects of interest in an image are identified. This is difficult to achieve and verify for medical images. We present a method for the transformation of real data to train any Deep Neural Network to solve the above problems. We show the efficacy of this approach on both a weakly supervised localization model and a strongly supervised localization model. For the weakly supervised model, we show that the localization accuracy increases significantly using the generated data. For the strongly supervised model, this approach overcomes the need for exhaustive annotation on real images. In the latter model, we show that the accuracy, when trained with generated images, closely parallels the accuracy when trained with exhaustively annotated real images. The results are demonstrated on images of human urine samples obtained using microscopy.

翻译：尽管人工智能（AI）在医学图像分析中的应用日益广泛，但由于数据和专家标注的有限可用性，医学领域生成标注数据所需的专业知识、时间和成本仍然非常高。强监督目标定位模型需要的数据需经过详尽标注，即图像中所有感兴趣的目标均被识别。这一要求在医学图像中难以实现和验证。我们提出了一种将真实数据转换的方法，以训练任意深度神经网络来解决上述问题。我们在弱监督定位模型和强监督定位模型上展示了该方法的有效性。对于弱监督模型，我们发现使用生成的数据后定位精度显著提升。对于强监督模型，该方法克服了对真实图像进行详尽标注的需求。在后一种模型中，我们表明，使用生成图像训练的精度与使用详尽标注真实图像训练的精度高度接近。相关结果通过使用显微镜获取的人类尿液样本图像进行了演示。