## 1 Introduction  
Intracerebral hemorrhage (ICH), an untreatable and severe form of brain hemorrhage, is a serious problem where one-third of patients die within a month, and even survivors may experience neurological complications [1, 2]. Especially if rapid diagnosis and prompt treatment are not performed, the mortality rate of ICH patients can be increased [3]. Despite the seriousness of ICH, the incidence rate of ICH is steadily increasing. [PERSON] et al. reported that the incidence rate of ICH increased by 11% over 15 years across the United States. They particularly highlighted the rising incidence of ICH among young economically-active and middle-aged populations in the United States, emphasizing the need for prevention strategies for ICH targeting [4]. In this situation, [PERSON] et al. investigated the impact and importance of neuroimaging examinations for ICH patient management [5]. This is because, for the prompt diagnosis and identification of the underlying cause of the ICH, neuroimaging examinations are essential. In particular, the results of neuroimaging examinations are used to prioritize patients and determine appropriate medical treatment. However, the individuals responsible for conducting these neuroimaging examinations and documenting the results are radiologists. In that situation, the Royal College of Radiologists stated that while the number of CT and MRI scans performed has increased by 7% annually,there has only been a 4% annual increase in the number of radiologists [6]. This shortage of radiologists leads to delayed diagnosis and medical treatment. In such circumstances, [PERSON] et al. highlighted that innovative technologies like automated ICH detection can be rapidly applied in the diagnostic stage of ICH [5]. In a similar context, [PERSON] et al. stated that artificial intelligence techniques can analyze CT scans with high accuracy and speed, and it can be helpful for experts, radiologists, and patients [3]. This is why various medical image captioning studies have been conducted in the past. Using medical images as input, medical image captioning generates corresponding text. In the imageCLEFmedical Caption Task 2022, organized by Cross-Language Education and Function (CLEF), a medical image captioning challenge was held using datasets consisting of single CT images, MRI images, X-ray images, and their corresponding captions [7]. In this competition, [PERSON] et al. utilized a ResNet-50-based multi-label classification model, treating words as individual labels, and achieved the highest performance with a BLEU score of 0.48 [8]. In the same challenge, [PERSON] et al. utilized an encoder-to-decoder model for medical image captioning [9]. They used vision transformers and convolutional vision transformers as encoders and BERT, distilGPT2 models as decoders, with the method that sets the probability for certain tokens to 0 to address the n-gram repetition problem. Moreover, [PERSON] et al. proposed the medical image captioning model, which combines the output vectors of the show-and-tell model and GPT-3 on the OPEN-I [10] and MIMIC-CXR [11] datasets [12]. Such studies have achieved success in single medical image captioning, but there is a limitation to applying such studies to diseases, which mainly use CT scans that represent 3D images in sequential 2D images. This is due to the characteristics of CT scans, which disperse 3D space information into sequential 2D images. Due to such limitations, it is difficult to find studies that have conducted captioning tasks for brain CT scans [13]. At the same time, unlike single medical image captioning, which considers the information from the single medical image, captioning for CT scans is a task that requires the model to generate text based on the information from all the images that make up the CT scan. However, CT scans are ready in most situations and can provide clues to determine the main cause of ICH even when there is little patient information [14; 15].