# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Adversarial Methods
The model achieved an AUC score of 96.04 and an F1 score of 69.67 on the ISIC2018 dataset with 20% labeled data. On the Chest X-ray14 dataset, the method achieved AUC scores of 69.75, 74.50, 77.52, 79.49, and 80.69 on labeled percentages of 2%, 5%, 10%, 15%, and 20%. Lastly, the hip X-ray dataset generated an AUC score of 92.43 and an F1 score of 72.39 with 20% labeled data. Other state-of-the-art methods such as MT, SRC-MT, and VAT were also tested on this dataset achieving AUC scores of 92.00, 91.40, 91.66, and F1 scores of 70.52, 70.90, and 70.57, respectively. Semi-supervised Adversarial Classification (SSAC) [58]: this GAN-based method involves a reconstruction network R, and a supervised classification network C. Learnable transition (T) layers are used to transfer the image representation ability learned by network R to C. R is an adversarial autoencoder-based unsupervised network consisting of a generator G and a discriminator D. G contains an encoder and a decoder. The encoder, which has an architecture similar to ResNet [59], takes 64 \(\times\) 64 patches, and the decoder generates reconstructed patches of the same size. D is a deep convolutional neural network containing four convolutional layers. C contains two parts, one with the same architecture as the encoder of R, and one a fully connected layer with two neurons, separated by a global average pooling (GAP) layer. There is no parameter sharing between R and C; the learnable T layers, with each layer consisting of a \(1\times 1\) convolutional layer, transfers the feature maps obtained by R to the corresponding blocks in C. In the experimentation, C was pre-trained on ImageNet, and R was pre-trained on both the labeled and unlabeled data. The loss function was defined as follows:  
\[ \begin{array}{l}L_{SSAAC}(X_{m})\\ =\lambda_{1}\{l_{l\text{\emph{mse}}}(G(X_{m}),X_{m})-[l_{bc}(D(G(X_{m})),0)+l_{ bc}(D(X_{m}),1)]\}\\ +l_{bc}(C(X_{m}),Y_{m})\end{array}  \tag{18}\]where \(X_{m}\) is the \(m\)th input sample and \(\lambda_{1}\) is a weighting parameter. The three terms in the function represent the mean square reconstruction loss of G, the cross-entropy adversarial loss of D, and the supervised classification loss, respectively. Two datasets were used for experimentation. The first dataset was a database of lung nodules on CT-scans from the lung image database consortium (LIDC), and the image database resource initiative (IDRI) called LIDC-IDRI. The second dataset was the Tianchi Lung Nodule Detection dataset. The LIDC-IDRI dataset contains 1018 chest CT scans from which the nodules of diameter 3 mm to 30 mm were identified and annotated. From the Tianchi Lung Nodule Detection dataset, 1000 patients' data was used to extract 1227 unlabeled nodules. In total, the combination of the two datasets resulted in 644 malignant, 1301 benign, and 1839 unlabeled samples, making this a single-label classification task. For this task, the MK-SSAC model was constructed, which consists of nine knowledge-based collaborative SSAC models (KBC-SSAC) trained on the patches extracted from the lung nodules whose predictions were subsequently combined by two output neurons. The model was tested five independent times with 10-fold cross-validation. The metrics calculated to gauge the results were accuracy, sensitivity, specificity, and AUC. The performance of the model was compared to other SSL methods (MK-CatGAN, MK-AAE, MK-Ladder Network), hand-crafted feature-based methods (3D GLCM + SVM, MVF + SVM), and DCNN-based methods (Fuse-TSD, MV-KBC). The average scores achieved by the model were 92.53 accuracies, 84.94 sensitivity, 96.28 specificities, and 95.81 AUC. The model obtained the highest accuracy and AUC scores as well as the second-highest sensitivity and specificity among all the methods compared. Additionally, the MK-SSAC model was tested with different percentages of the labeled data (100%, 80%, 60%, 40%, and 20%). From 20% to 100%, the scores increased by about 2% for accuracy and 3% for AUC.