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Intracerebral hemorrhage (ICH) is a severe cerebrovascular disorder that poses a life-threatening risk, necessitating swift diagnosis and treatment. While CT scans are the most effective diagnostic tool for detecting cerebral hemorrhage, their interpretation typically requires the expertise of skilled professionals. However, in regions with a shortage of such experts or situations with time constraints, delays in diagnosis may occur. In this paper, we propose a method that combines a pre-trained CNN classifier and GPT-2 to generate text for sequentially acquired ICH CT images. Initially, CNN undergoes fine-tuning by learning the presence of ICH in publicly available single CT images, and subsequently, it extracts feature vectors (i.e., matrix) from 3D ICH CT images. These vectors are input along with text into GPT-2, which is trained to generate text for consecutive CT images. In experiments, we evaluated the performance of four models to determine the most suitable image captioning model: (1) In the N-gram-based method, ReseNet50V2 and DenseNet121 showed relatively high scores. (2) In the embedding-based method, DenseNet121 exhibited the best performance. (3) Overall, the models showed good performance in BERT score. Our proposed method presents an automatic and valuable approach for analyzing 3D ICH CT images, contributing to the efficiency of ICH diagnosis and treatment.

Intracerebral hemorrhage is a severe problem where more than one-third of patients die within a month. In diagnosing intracranial hemorrhage, neuroimaging examinations are essential. As a result, the interpretation of neuroimaging becomes a crucial process in medical procedures. However, human-based image interpretation has inherent limitations, as it can only handle a restricted range of tasks. To address this, a study on medical image captioning has been conducted, but it primarily focused on single medical images. However, actual medical images often consist of continuous sequences, such as CT scans, making it challenging to directly apply existing studies. Therefore, this paper proposes a CT image captioning model that utilizes a 3D-CNN model and distilGPT-2. In this study, four combinations of 3D-CNN models and language models were compared and analyzed for their performance. Additionally, the impact of applying penalties to the loss function and adjusting penalty values during the training process was examined. The proposed CT image captioning model demonstrated a maximum BLEU score of 0.35 on the in-house dataset, and it was observed that the text generated by the model became more similar to human interpretations in medical image reports with the application of loss function penalties.

The tendon that is inserted into the duct is a crucial component of prestressed concrete (PSC) bridges and, when exposed to air, can quickly corrode, and cause structural collapse. It can interpret the signal measured by non-destructive testing (NDT) to determine the condition (normal or void) inside the duct. However, it requires the use of expensive NDT equipment such as ultrasonic waves or the hiring of experts. In this paper, we proposed an impact–echo (IE) method based on deep support vector data description (Deep SVDD) for economical void detection inside a duct. Because the pattern of IE changes for various reasons such as difference of specimen or bridge, supervised learning is not suitable. Deep SVDD is classified as normal and defective, which is a broad distribution as a hypersphere that encloses a multi-dimensional feature space for normal data represented by an autoencoder. Here, an autoencoder was developed based on the ELMo (embeddings from language model)-like structure to obtain an effective representation for IE. In the experiment, we evaluated the performance of the IE data measured in different specimens. Thus, our proposed model showed an accuracy of about 77.84% which is an improvement of up to about 47% compared to the supervised learning approach.

Language development is inextricably linked to the development of fundamental human abilities. A language problem can result from abnormal language development in childhood, which has a severe impact on other elements of life. As a result, early treatment of language impairments in children is critical. However, because it is difficult for parents to identify atypical language development in their children, optimal diagnosis and treatment periods are frequently missed. Furthermore, the diagnosis process necessitates a significant amount of time and work. As a consequence, in this study, we present a deep learning-based language development screening model based on word and part-of-speech and investigate the effectiveness of a large-scale language model. For the experiment, we collected data from Korean children by transcribing the utterances of children aged 2, 4, and 6 years. Convolutional neural networks and the notion of Siamese networks, as well as word and part-of-speech information, were used to determine the language development level of children. We also investigated the effectiveness of employing KoBERT and KR-BERT among Korean-specific large-scale language models. In 5-fold cross-validation study, the proposed model has an average accuracy of 78.0%. Furthermore, contrary to predictions, the large-scale language models were shown to be ineffective for representing children’s utterances.