# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Other Methods
Existing SSL methods cannot be used reliably for FL as they depend on the labeled dataset being accessible during training. However, in the case of FL, the local data at a particular location may be entirely unlabeled. As such, [PERSON] et al. [74] build an interaction between the learning of labeled and unlabeled data by leveraging inherent disease relationships which are independent of a specific local dataset. This client-invariant disease relation information can be extracted from the labeled data and used to supervise the learning of unlabeled data to ensure it captures similar disease relationships. An uncertainty-based algorithm is used to filter out unreliable pseudo-labels. The backbone FL framework of this method follows the standard FL paradigm in which the central server sends the global parameters \(\theta\) to every client \(k\). The model is trained at each \(k\) with a local objective function \(\mathcal{L}^{k}\) and the updated local parameters are sent back to the central server and aggregated to update the global model. In the proposed model, local parameters are aggregated using weights proportional to the size of their database following the federated averaging algorithm (FedAvg) method [75]. The local learning objectives use the cross-entropy loss at labeled clients and the consistency regularization mechanism at unlabeled clients with transformations such as rotation, translation, and flip. The inter-client relation matching (IRM) method is introduced for disease relation estimation to assist learning at unlabeled clients, in which the disease relation matrix \(\mathbb{M}^{l}\) is derived from the data's pre-SoftMax features at each labeled client and averaged to obtain the matrix \(\mathbb{M}\) representing the general release relation. The model was tested on two datasets; ISIC2018 and the RSNA 2019 Brain CT dataset. From the RSNA dataset, 25,000 slices were sampled and divided into 70%, 10%, and 20% splits for training, validation, and testing. The ISIC2018 images were resized to 224 \(\times\) 224 and split similarly. The FL setting was simulated by partitioning the training set into 10 local clients, of which 20% (two clients) were labeled. DenseNet121 was used as the backbone model. On the RSNA dataset, the model achieved an AUC score of 87.56 and F1-score of 59.86. On the ISIC2018 dataset, it achieved an AUC score of 92.46 and F1-score of 55.81. Semi-HIC [76]: [PERSON] et al. [76] propose an SSL method called Semi-HIC for the classification of histopathological images, which are representations of biopsy samples captured by a microscope. This model used a patch-wise CNN to learn embeddings of labeled and unlabeled images, wherein three convolutional blocks are used to extract low-level detail information, and four cascaded inception blocks are used to create discriminative representations for patches. Semi-HIC uses a novel loss function obtained from combining an association cycle consistency (ACC) loss and a maximal conditional association (MCA) loss. These losses are intended to address the two major challenges in histopathological image classification, which are inter-class similarity and intra-class variation. They are based on the walker loss and visit loss proposed by [PERSON] et al. [39] in their LA method. The original visit loss is vulnerable to the inter-class similarity in histopathological images, as a cancerous labeled embedding \(E_{i}^{l}\) and unlabeled embedding \(E_{k}^{u}\) with the underlying non-cancerous class may have high similarity. [PERSON] et al. 's MCA loss aims to address this by separately calculating the visit probability of non-cancerous embeddings \(p_{k}^{un}\) and the visit probability of cancerous embeddings \(p_{k}^{vc}\). If the unlabeled embedding \(E_{k}^{u}\) has an underlying cancerous class, it is expected to have a high \(p_{k}^{vc}\) and low \(p_{k}^{un}\) and vice versa.