# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Hybrid Methods
Testing was conducted at different percentages of labeled data (5, 10, 25, 50, 100 for MuNuSeg and 1, 5, 10, 100 for Ki-67). For the MoNuSeg dataset, the proposed method achieved higher scores than both the supervised baseline and the Mean Teacher for all percentages, notably in the 5% and 10% percentages, where it obtained scores approximately 2% higher than the Mean Teacher. The highest score obtained was 76.89 for 50% of labeled data, and the lowest was 75.02 for 5% of labeled data. In the case of the Ki-67 dataset, the proposed method also achieved a higher score than the supervised baseline and Mean Teacher, the highest increase from Mean Teacher being approximately 2.5% at the 5% labeled percentage. The highest score was 79.79 for 10% of labeled data, and the lowest was 74.9 for 1% of labeled data. Deep virtual adversarial self-training with consistency regularization [80]: This model is both adversarial and consistency-based. It consists of three parts: self-training, consistency regularization, and virtual adversarial training. As previously mentioned, self-training involves using the model itself to generate labels for unlabeled samples, which will then become part of the labeled training set in the next iteration. The model outputs a probability distribution over all classes, and the generated label is only kept when the highest probability is above a predetermined threshold. Consistency enforcement is applied to both labeled and unlabeled samples. For the labeled samples, weak augmentation is applied, and the generated label is encouraged to be consistent with the ground truth label. For unlabeled samples, weak augmentation is applied, then a pseudo-label is generated by the self-training model; the same input image is then strongly augmented, and the new prediction must be consistent with the previously obtained pseudo-label. Lastly, virtual adversarial training is applied to improve the robustness of the model and strengthen its generalization capability. Thus, the loss function of the model becomes the weighted sum of three losses: the supervised cross-entropy loss \(\ell_{\rm s}\) for labeled data, the regularization loss \(\ell_{\rm r}\) for unlabeled data and the virtual adversarial training loss \(\ell_{\rm vat}\) applied to both labeled and unlabeled data. \[\mathcal{L}=\ell_{\rm s}+\alpha\cdot\ell_{\rm r}+\beta\cdot\ell_{\rm vat} \tag{37}\]  
where \(\alpha\) and \(\beta\) are the weighting coefficients. Experimentation was performed using two datasets; a breast ultrasound (BRUS) dataset and an optical coherence tomography (OCT) dataset. The BRUS dataset contains 39,904 images, 22,026 of which are labeled as malignant (14,557) or benign (7469). The other 17,878 images are unlabeled. The labeled data were randomly split into 34% training (7662), 34% validation (7579), and 32% testing (6785). The OCT dataset contains 109,309 images, each labeled with four different types (three diseases and one normal). There were 20,000 images taken for the validation and testing datasets, respectively, and 1% of the remaining data (691 images) was taken for the labeled dataset, whereas the rest was considered unlabeled. The network architecture used for this experimentation is DenseNet-121. The weak augmentations include horizontal and/or vertical flips with a 50% probability, as well as randomly applied horizontal and/or vertical translations by less than 20% of the image width or height. Strong augmentation includes transformations such as color channel reduction, contrast maximization, solarization, rotation, and posterization. The results are compared with Mean Teacher, VAT, MixMatch [42], and FixMatch [44], as well as the fully supervised DenseNet-121 as the baseline. The metrics calculated were the accuracy, F1, AUC, and Kappa scores. The proposed method achieved higher scores than all the compared methods on both datasets, with an F1-score of 0.88 on the BRUS dataset and a Macro F1-score of 0.91 on the OCT dataset.