There has been a steady escalation in the impact of Artificial Intelligence (AI) on Healthcare along with an increasing amount of progress being made in this field. While many entities are working on the development of significant deep learning models for the diagnosis of brain-related diseases, identifying precise images needed for model training and inference tasks is limited due to variation in DICOM fields which use free text to define things like series description, sequence and orientation [1]. Detecting the orientation of brain MR scans (Axial/Sagittal/Coronal) remains a challenge due to these variations caused by linguistic barriers, human errors and de-identification - essentially rendering the tags unreliable [2, 3, 4]. In this work, we propose a deep learning model that identifies the orientation of brain MR scans with near perfect accuracy.

中文翻译：

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使用深度学习检测脑 MR 扫描的方向

人工智能 (AI) 对医疗保健的影响一直在稳步升级，并且在该领域取得了越来越多的进展。虽然许多实体正在开发用于诊断大脑相关疾病的重要深度学习模型，但由于 DICOM 领域的变化，识别模型训练和推理任务所需的精确图像受到限制，这些领域使用自由文本来定义系列描述等内容，顺序和方向 [1]。由于语言障碍、人为错误和去识别化导致的这些变化，检测大脑 MR 扫描的方向（轴向/矢状面/冠状面）仍然是一个挑战 - 基本上使标签不可靠 [2, 3, 4]。在这项工作中，