输入关键词或点击标签，按论文、数据资源、竞赛截止日期、征稿与课程缩小范围。 标签：Medical Image 范围：数据资源
AMOS is an abdominal multi-organ segmentation benchmark with CT and MRI cases for evaluating versatile medical image segmentation models. It supports abdominal organ segmentation, modality-general segmentation, and benchmarking of robust 3D segmentation methods.
MedMNIST v2 is a standardized collection of lightweight biomedical image classification datasets, including 2D and 3D tasks. It is useful for quick benchmarking, AutoML, foundation model sanity checks, and reproducible evaluation across multiple medical imaging domains.
SLAKE is a semantically labeled medical visual question answering dataset with bilingual English-Chinese questions, medical images, and knowledge-enhanced annotations. It is useful for medical multimodal learning, image-grounded QA, and radiology VQA evaluation.
Legacy multi-task biomedical image segmentation benchmark retained as a reference; newer segmentation benchmarks are listed above it.
Foundation model and toolkit for all-in-one biomedical image parsing across recognition, detection, and segmentation tasks.
Google Health AI Developer Foundations open model resources for medical text and medical image understanding, including MedGemma 1.5 resources.
Interactive medical image segmentation benchmark and baseline from CVPR 2025, covering multiple modalities, organs, and target structures.