# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Hybrid Methods
\(w_{k}^{b}\) is calculated as follows:  
\[w_{k}^{b}=\frac{1}{Z}\sum_{i}\sum_{j}\frac{\partial Y^{b}}{\partial A_{ij}^{k}} \tag{39}\]  
where \(A_{ij}^{k}\) is the pixel value at location (\(i\), \(j\)) of \(k\), \(Z\) represents the number of pixels in \(k\) and \(Y^{b}\) denotes the maximum prediction score of batch \(b\) generated by the classification model. Random threshold \(\lambda\in[0,1]\) is applied to the gray-level \(GradMaxCam^{b}\) to obtain the binary mask CamMask. The pixels higher than \(1-\lambda\) are set to 1, and all others are set to 0. The CamMix algorithm takes a batch of labeled and unlabeled data and their corresponding predictions, including weak and strong augmentations of the data, and generates a mixed batch of original samples and shuffled samples, whose corresponding labels are mixed based on the number of pixels in CamMask. Thus, considering the original samples \(input_{1}\) and the shuffled samples \(input_{2}\)=\(input[rand\_index]\) as well as the corresponding label targets \(target_{1}\) and \(target_{2}\), the CamMask is obtained by calculating \(\text{GradMaxCam}(input_{1}\,1-\lambda)\), and the parameter \(lam\) is calculated based on the number of pixels in the CamMask as follows:  
\[ \begin{split} lam=sum(\text{CamMask}\ ==1)/\\ (\text{CamMask.size}(0)*\text{CamMask.size}(1))\end{split}  \tag{40}\]  
The mixed batch \(mixed\_input\) is obtained from \(input_{1}\) and \(input_{2}\) by calculating the following:  
\[ \begin{split} mixed_{input}=\\ input_{1}*\text{CamMask}+input_{2}*(1-\text{CamMask})\end{split}  \tag{41}\]  
Lastly, the overall loss of the model is defined as:  
\[ \begin{split} 1=criterion(\text{logits},\ target_{1})\ *\ lam+\\ criterion(\text{logits},\ target_{2})\ *(1-\ lam)\end{split}  \tag{42}\]  
wherein logits = model(\(mixed\_input\)). The model was tested with a ResNet-18 on two datasets: The Interstitial Lung Disease (ILD) dataset and the ISIC2018 dataset. Instead of the F1-score, the metric used for evaluation was \(F_{avg}\), the average F1-score over the different classes. The ILD dataset contains 109 CT scans with an average of 25 slices per case for a total of 2795 slices. All regions of interest were cropped into patches of size 32 \(\times\) 32, resulting in approximately 7000 labeled patches and 19,000 unlabeled patches. Six typical lung patterns were used for the classification task. By using CamMix, the model achieved an \(F_{avg}\) score of 95.34, higher than previous state-of-the-art MSDA methods for the ILD dataset, such as MixUp, CutMix [84], and Fmix [85]. For the ISIC2018 dataset, the model was trained with 20% labeled and 80% unlabeled data, obtaining an AUC score of 94.04 and \(F_{avg}\) of 78.15. These scores were higher than the scores obtained by the previously mentioned MSDA methods. The SRC-MT model was also used for comparison and achieved a \(F_{avg}\) score of 78.15, higher than the one obtained by CamMix. On the other hand, it obtained a lower AUC score. A summary of methods discussed in this section is provided in Table 5. Three hybrid methods have been reviewed: Local and Global Consistency Regularized, PLGAN, SSL with CamMix, and [PERSON] et al. 's method. We have reported the SSL methods which have been combined to create each hybrid method, as well as the datasets used for testing each model and the best performance score obtained on each dataset.