# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Consistency-Based Methods  
Relation-driven Self-Ensembling Model (SRC-MT) [19]: This method employs the Mean Teacher framework with a consistency-enforcing approach. In addition, this method explores the intrinsic relation between the images, which is generally overlooked in consistency-based methods, including Mean Teacher. Studying the relation between the images in an unsupervised fashion helps extract useful information from the unlabeled data [47, 48]. The SRC-MT method proposes a novel Sample Relation Consistency (SRC) paradigm which enforces the consistency of the relation between two images after perturbation; if two images have a similarity before perturbation, this relation should be preserved after they are perturbed. In other words, the relationship between input samples \(x_{1}\) and \(x_{2}\) must be the same as the relationship between perturbed samples \(x_{1}\)\({}^{\prime}\) and \(x_{2}\)\({}^{\prime}\). Thus, this  
Figure 2: Diagram of the types of SSL approaches and the datasets used to test each method. method achieves consistency in both labeling and relations after perturbation. The total objective functions of the framework are as follows:  
\[\mathcal{L}=\mathcal{L}_{s}+\lambda\mathcal{L}_{u},\text{ with }\mathcal{L}_{u}= \mathcal{L}_{c}+\beta\mathcal{L}_{src} \tag{9}\]  
wherein \(\mathcal{L}_{s}\) is the supervised objective and \(\mathcal{L}_{u}\) the unsupervised objective. \(\mathcal{L}_{u}\) is made up of the standard consistency loss \(\mathcal{L}_{c}\), and the relation consistency loss \(\mathcal{L}_{src}\). \(\lambda\) is the trade-off weight between unsupervised and supervised loss, and \(\beta\) is a hyperparameter to balance \(\mathcal{L}_{c}\) and \(\mathcal{L}_{src}\). SRC-MT was tested on two public benchmark medical image datasets; thorax disease classification with ChestX-ray14 for multilabel classification and ISIC2018: Skin Lesion Analysis for single label classification. Perturbations such as random rotation, horizontal and vertical flips, and translation were applied to the input images for consistency enforcing. For the skin legion dataset, the images were all resized to 224 \(\times\) 224. The dataset was divided into 70% training, 10% validation, and 20% testing. DenseNet121 [23], pre-trained on ImageNet [49], was used as the network backbone. For the ChestX-ray14 dataset, the images were resized to 384 x 384, and the same data split as the previous dataset was used. DenseNet169 [23], pre-trained on ImageNet, was used as the network backbone. For comparison, the upper bound and baseline performances were calculated; the upper bound was the performance of the fully supervised model trained on 100% labeled data, and the baseline was the performance of the fully supervised model trained on 20% of the data. Additionally, the model was compared to other semi-supervised learning methods, including the self-training-based method [50], SS-DCGAN [51], the Temporal Ensembling method, and the Mean Teacher. The training set used for the proposed method consisted of 20% labeled and 80% unlabeled data. The metrics used to gauge the performance of the model for single-label classification was AUC, sensitivity, specificity, accuracy, and F1 scores. For the single-label classification task on the skin legion dataset, SRC-MT achieved the highest AUC, sensitivity, accuracy, and F1 scores compared to the baseline and other semi-supervised models. SRC-MT also achieved the second-highest specificity. Additionally, the AUC score of SRC-MT (93.58) was 3% higher than the baseline and 2% less than the upper bound and the F1 score (60.68) was 8% more than the baseline and 10% less than the upper bound. For the multi-labeled classification on the Chest X-ray14 dataset, the metric used was AUC, and for comparison the upper bound and baseline as well as the GraphXNET[34] method were used. The AUC scores were calculated for each of the 14 labels and averaged.