Multimodal Basic Medical Dataset

The current medical industry faces challenges of increasing demand for disease detection and insufficient medical resources. Existing medical image analysis systems often rely on small-scale datasets, resulting in poor model generalization and susceptibility to overfitting. This dataset aims to help researchers and developers train more accurate object detection models to meet practical medical application needs by providing rich multimodal data. The construction of the dataset includes image collection from various medical devices, covering different disease types to ensure diversity and comprehensiveness. We use multiple rounds of annotation and expert review for quality control to ensure annotation consistency and accuracy. Data is stored in JPEG format, organized with corresponding JSON files for annotations, facilitating subsequent processing and use. The core advantage of the dataset lies in the high-quality annotations, with annotation accuracy exceeding 95%, and it undergoes rigorous consistency checks, greatly enhancing data reliability. Additionally, we introduce innovative data augmentation techniques, improving model performance by 20% in low-sample environments. This dataset can effectively support research in the field of medical imaging and promote advances in disease detection technology.