# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 4 Detection of CISs Based on Deep Learning Methods
### Classification
#### 4.1.2 Semi-Supervised Learning in Classification  
Training deep classification models needs a vast quantity of annotated data. Unlike natural images, annotating medical images is a costly and time-consuming process. To deal with such a problem, the major two strategies are data augmentation [35] and transfer learning [36]. In the traditional data augmentation approach, a number of visual transformations of an image are employed to create images, which can introduce diversity into the training data to some extent and minimize the risks of overfitting of the models. However, the diversity given by visual transformation is still not enough; besides, these transformations influence the distribution of the original dataset and introduce additional noise and offset. Transfer learning needs pretraining on a large-scale natural image dataset and then fine-tuning on the target dataset; however, fine-tuning the CNN model on a restricted dataset can easily lead to overfitting.  
To relieve the dependency of manually labeled data, a promising technique named semi-supervised learning (SSL) has attracted increased attention in recent years, which clearly highlights the potential usefulness of employing unlabeled data. Previous lung CT CISs detection approaches based on semi-supervised learning are generally based on GAN [37], which can learn from existing data to produce high-quality pictures, enabling cross-modal data production and image-to-image conversion. He [38] used GAN to create a large number of high-quality CISs samples to expand the training data in order to improve the classification performance of the signs. Unlike transfer learning approaches, the method does not acquire new information from other datasets; the classification model does not need to be retrained and fine-tuned; both the GAN and the CNN are trained on the same dataset; and the distribution of data is consistent. The strategy enhances the accuracy and specificity of the model by 6.7%, 12.8%, 6.6%, and 5.7%, respectively, compared to typical data augmentation [35] and transfer learning methods [36].  
While the method described above is an enlightening sign for semi-supervised learning based on sign detection, there are still some issues. The performance of the classification model in the above work is strongly reliant on the quality of the created CISs, and the high-quality CISs samples in this work are generated once and will not be updated. Addingthe generated false positive samples into the subsequent training will introduce severe overfitting, which directly degrades the classification performance. To overcome this issue, [PERSON] [39] designed an enhanced DCGAN framework based on this GAN to produce simulated samples of unlabeled data. In contrast to the one-off synthesis of samples by [PERSON] et al., this study continually updates the quality of the created CISs, thereby decreasing the influence of the generated false positive samples on the following classification performance. As the above two methods are for different kinds of signs, the classification results cannot be compared directly, but overall, the above research shows that GAN can provide a promising solution for sign recognition when sign samples are limited; however, the related research has not received sufficient attention, which is worth further exploration by researchers.