## 2 The Papers
### Applications-Other  
In the last category we included three other applications based on medical imaging, related to cancer, gastrointestinal disorders, and respectively medical report generation. In the first paper, the authors try to optimize the radiological workload, by using the knowledge graph method, a novel method that enhances search engines in general proposed by Google in 2012 [17]. Firstly, there is an initial knowledge association between disease labels, that are defined as nodes. This is done in two steps, with the help of CheXpert tagger, that classifies the reports into 14 different categories, and the SentencePiece tagger/de-tagger tool, from which the nouns with top k occurrences are selected as additional disease categories. Based on this, a graph convolutional neural network is used to aggregate information between nodes, creating prior knowledge. This is done by generating a hybrid image-text feature, with features extracted from X-ray images with the help of a CNN, and text features extracted from the associated clinical reports using transformers. The transformers represent a better option compared to the classic RNN approach, as radiology reports tend to consist of longer sentences. This hybrid pair is sent through the graph convolutional network, and the node features are split into two branches: a linear classifier for disease classification and a generator head for the report itself. The result is fine-tuned by re-running it through the text classifier. The results are evaluated by the quality of the Natural Language Generation and as well as the clinical efficacy.  
The second paper uses the DDSM dataset (Digital Database for Mammography Screening), with a total of 2620 films, of which 695 are normal and 1925 are abnormal, to improve the detection of Breast Cancer, a pathology encountered worldwide, which puts at risk many lives [18]. The employed model, unlike the classic CNN architectures such as VGG, ResNet, MobileNet, etc. is represented by the novel CoroNet: based on the Xception CNN architecture, which consists of a 71-layer deep CNN architecture, pretrained on an ImageNet dataset. The key efficiency improvement of this architecture is the depth wise separable convolution layers with residual connections, which enable a decrease in the number of operations. Separable convolution replaces the classical n \(\times\) n \(\times\) k convolution with a 1 \(\times\) 1 \(\times\) k point-wise convolution, followed by a channel-wise n \(\times\) n spatial convolution, and the residual connections represent \"skip connections\" which enable the flow of gradients without the need for non-linear functions of activation. This mitigates the disappearing gradient issue. As per the results, this solution outperforms alternative networks.  
Finally, in the third paper, a CNN backbone, ResNet-50 pre-trained on ImageNet, is used to extract features from static images, and a GCN (Graph Convolutional Network) is employed for classifying the relationship between labels [19]. An LSTM architecture was used for the temporal association between subsequent frames in the gastroscopy. Those form the proposed GL-Net architecture, which combines the label extraction feature and temporal correlation and dependencies, for real-time predictions in gastroscopy videos. The dataset consists of 49 videos and after video processing and 5 Hz sampling, 23,471 training images and 5798 test images are obtained, with multi-label annotations.