# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Consistency-Based Methods
The SRC-MT achieved a score of 79.23, approximately 0.4% higher than the Mean Teacher, 1.2% higher than GraphXNET, 2% higher than the baseline, and 2% lower than the upper bound. NoTeacher (NoT) [52]: In the Mean Teacher, the consistency target, which is the teacher, relies on the EMA of the student. In other words, the teacher's weight is an ensemble of student weights. As such, the weights of the student and teacher are tightly coupled. This leads to the model being vulnerable to confirmation bias [53]; the teacher continues to reinforce what it already believes. NoTeacher is a consistency-based SSL framework that addressed this issue by employing two independent networks, thus removing the need for the time-averaging component. The NoTeacher framework works as follows: two random augmentations are applied to an input \(x\), which can be a 2D or 3D sample. These augmentations result in samples \(x_{1}\) and \(x_{2}\), which becomes the input of \(F_{1}\) and \(F_{2}\), two networks with similar architectures. The outputs are named \(f_{1}^{L}\) and \(f_{2}^{L}\) for labeled inputs and \(f_{1}^{U}\) and \(f_{2}^{U}\) for unlabeled inputs. Next, a loss function is calculated to enforce prediction consistency between \(F_{1}\) and \(F_{2}\). This loss function consists of a consistency loss and a supervised cross-entropy loss; if \(x_{1}\) and \(x_{2}\) are augmented versions of the same input \(x\), then \(f_{1}\) resulting from \(x_{1}\) and \(f_{2}\) resulting from \(x_{2}\) must be similar. Additionally, if \(x\) is a labeled input, then the outputs of both networks should match the ground truth target \(y\). Lastly, the total loss is backpropagated to update network parameters. The NoTeacher method is like the Mean Teacher as they both employ two networks with similar architectures. However, the NoTeacher method introduces two differences; first, it removesthe EMA, making the networks entirely independent, and second, NoTeacher bases its loss function on a graphical model. The graphical model involves a graph consisting of \(f_{1}\), \(f_{2}\) and \(y\) as the nodes, each connected to a consensus function \(f_{c}\in[0,1]\), which enforces mutual agreement of the outputs on labeled and unlabeled data. The three datasets used for testing this model were Chest X-ray14, Radiological Society of North America (RSNA) Brain CT, and the Knee MRNet dataset containing knee MRI exams. The backbone networks used for each dataset were DenseNet121 [23], DenseNet169 [23], and MRNet [54], respectively, all pre-trained on ImageNet. All inputs were normalized based on ImageNet statistics before training. For all the datasets, the experiment was repeated six or seven times with different percentages of the dataset being labeled. The Chest X-ray14 dataset was used for multi-label classification. It was divided into 70% training, 10% validation, and 20% testing. The AUROC scores of the method were compared to the supervised baseline as well as other semi-supervised methods, including Mean Teacher, VAT, Pseudo-labeling, and the previously discussed SRC-MT. The NoTeacher method consistently achieved higher scores than the previous methods. It achieved a score of 82.10 at 50% labeled data, 1% less than the supervised model at 100% labeled data. The SRC-MT method attained lower scores than MT at all labeling budgets below 20%, but a higher score at 20%. The RSNA Brain CT dataset was used for multi labeled classification. It includes 19,520 CT brain scans from the RSNA 2019 challenge. Each slice in these scans is labeled with five binary labels corresponding to the absence or presence of five different forms of hemorrhage. 14.3% of the images include the presence of at least one form of hemorrhage, with 30.1% including multiple forms. 85.7% of the data are samples with no abnormalities. The dataset was divided into 60% training, 20% validation and 20% testing. NoTeacher achieved consistently higher scores than both the supervised baseline and MT. It also achieved overall higher scores than the other semi-supervised methods.