数据集导航 · 通用医学数据集
通用医学数据集数据集(第 3 页)
思陌医疗数据服务整理的通用医学数据集公开数据集,共 200 个,覆盖该领域多模态数据,助力医疗AI研发选型。
全部
肿瘤 579
神经系统疾病 507
心血管系统疾病 462
耳鼻咽喉疾病 451
眼病 412
皮肤与结缔组织疾病 399
泌尿生殖系统疾病 362
内分泌系统疾病 352
医学文本与大模型 285
呼吸道疾病 245
口颌系统疾病 219
消化系统疾病 200
免疫系统疾病 137
通用医学数据集 136
血液与淋巴系统疾病 106
感染 96
创伤与损伤 66
肌肉骨骼疾病 14
化学诱发性障碍 4
环境因素所致疾病 3
共 136 个数据集 · 第 3 / 5 页
| 数据集名称 | 系统分类 | 病种 | 数据模态 | 任务类型 | 描述 | 来源 | 原始地址 | 操作 |
|---|---|---|---|---|---|---|---|---|
| eICU-CRD | 通用医学数据集 | 重症监护研究 | Detailed critical care data for over 200,000 admissions at 200+ hospitals across US | PhysioNet | https://eicu-crd.mit.edu/ | 访问 | ||
| HCUP US Hospitalization Data | 通用医学数据集 | 医疗利用分析 | Nationwide inpatient & emergency data for healthcare utilization and cost analysis | HCUP | https://hcup-us.ahrq.gov/ | 访问 | ||
| PSG-IPA | 通用医学数据集 | 睡眠评估 | PolySomnoGraphic Inter-scorer Performance Assessment, sleep and cognitive impairment assessment v1.0.0 2026 | Zenodo | https://physionet.org/content/psg-ipa/1.0.0/ | 访问 | ||
| MIMIC-III-Ext-PPG | 通用医学数据集 | 心肺分析 | PPG benchmark dataset for cardiorespiratory analysis from MIMIC-III v1.0.0 2026 | PhysioNet | https://physionet.org/content/mimic-iii-ext-ppg/1.0.0/ | 访问 | ||
| MultiCaRe | 通用医学数据集 | 多模态 | 临床病例 | Multimodal clinical case dataset with 70k+ case reports and 130k+ labeled images from oncology, cardiology, surgery, pat | PMC | https://zenodo.org/records/14994046 | 访问 | |
| 1000 Genomes Project | 通用医学数据集 | 群体遗传学 | Whole-genome sequencing for population genetics and variant analysis | IGSR | https://www.internationalgenome.org/data-portal/data-collection/30x-grch38 | 访问 | ||
| NSRR Sleep Datasets | 通用医学数据集 | 睡眠障碍检测 | Polysomnography & sleep signals for sleep disorder detection | NSRR | https://sleepdata.org/ | 访问 | ||
| Endometriosis Symptoms Monitoring Database | 通用医学数据集 | 症状监测 | Daily symptom tracking from 34 endometriosis patients over 1-10 months with MedDRA coding 2026 | Zenodo | https://physionet.org/content/ | 访问 | ||
| DATASUS TABNET | 通用医学数据集 | 公共卫生 | Official health information system of the Brazilian Ministry of Health | Brazil MoH | https://datasus.saude.gov.br/informacoes-de-saude-tabnet/ | 访问 | ||
| Portal de Dados Abertos do SUS | 通用医学数据集 | 公共卫生 | Open data portal for the Brazilian Unified Health System (SUS) | Brazil SUS | https://dadosabertos.saude.gov.br/ | 访问 | ||
| Global Health Observatory WHO | 通用医学数据集 | 全球健康统计 | WHO gateway to health-related statistics for 194 Member States | WHO | https://www.who.int/data/gho | 访问 | ||
| Global Health Data Exchange GHDx | 通用医学数据集 | 全球健康数据 | Comprehensive catalog of surveys, censuses, vital statistics, and health-related data | IHME | https://ghdx.healthdata.org/global-health-data-exchange | 访问 | ||
| Stanford AIMI Shared Datasets | 通用医学数据集 | 医学影像 | 影像共享 | Stanford AIMI shared medical imaging datasets platform | Stanford | https://aimi.stanford.edu/shared-datasets | 访问 | |
| MedVision | 通用医学数据集 | 医学影像 | 检测测量 | 30.8 million image-annotation pairs across 22 public datasets, CT/MRI/X-ray/PET detection & measurement 2025 | Project Page | https://arxiv.org/pdf/2511.18676 | 访问 | |
| SemBench | 通用医学数据集 | 语义查询 | 1400+ SPARQL templates for evaluating medical query engines, knowledge graph semantic query 2025 | GitHub | https://arxiv.org/pdf/2511.01716 | 访问 | ||
| MedAgentBoard | 通用医学数据集 | 多模态 | 多智能体协作 | 8 benchmark categories for multi-agent reasoning tasks, text/image/EHR multi-agent collaboration 2025 | Project Page | https://arxiv.org/pdf/2505.12371 | 访问 | |
| Lingshu Train | 通用医学数据集 | 多模态 | 多模态训练 | ~9.3M training samples from 60+ datasets, generalist foundation model for unified multimodal medical understanding 2025 | Project Page | https://arxiv.org/pdf/2506.07044 | 访问 | |
| MedEvalKit Lingshu test | 通用医学数据集 | 多模态 | 基准评测 | 152066 evaluation samples from 16 benchmark datasets, VQA, report generation, medical text QA 2025 | GitHub | https://arxiv.org/pdf/2507.04289 | 访问 | |
| RecGym Gym Workouts Recognition Dataset | 通用医学数据集 | 运动识别 | Gym workouts with IMU and Capacitive sensors, 10 volunteers, fitness recommendation 2025 | UCI | https://www.archive.ics.uci.edu/dataset/1128/recgym | 访问 | ||
| Visible Human Project | 通用医学数据集 | 解剖图谱 | Complete, anatomically detailed, 3D representations of human male and female bodies, NLM | data.gov | https://www.nlm.nih.gov/research/visible/visible_human.html | 访问 | ||
| PLACES Local Data for Better Health Place Data 2024 | 通用医学数据集 | 公共卫生 | CDC PLACES local health data, place-level estimates for 2024 release | data.gov | https://catalog.data.gov/dataset/places-local-data-for-better-health-place-data-2024-release-88989 | 访问 | ||
| VinDr-Mammo | 通用医学数据集 | 乳腺病变检测 | 5,000 four-view FFDM exams, BI-RADS + lesion annotations 2023 | PhysioNet | https://www.nature.com/articles/s41597-023-02100-7 | 访问 | ||
| RRTS Resource-limited colonoscopy benchmark | 通用医学数据集 | 结肠病变检测 | Colonoscopy resource-limited-setting benchmark with CAD labels 2025 | GitHub | https://github.com/Steventanardi/LuminaDX | 访问 | ||
| CADS Comprehensive Anatomical Dataset Segmentation | 通用医学数据集 | CT | 全身分割 | 22,022 CT scans, 167 comprehensive structures, largest scale, published July 2025 | Hugging Face | https://arxiv.org/abs/2507.22953 | 访问 | |
| SAROS | 通用医学数据集 | CT | 身体成分分析 | 900 CT scans, body regions/muscles/cavities, body composition, sparse annotation every 5th slice | TCIA | https://doi.org/10.25737/SZ96-ZG60 | 访问 | |
| VISCERAL Anatomy3 | 通用医学数据集 | 全身器官分割 | 20+ whole body CT/MRI volumes, 20+ organs, high-quality annotations | VISCERAL | http://www.visceral.eu/ | 访问 | ||
| LyNoS Lymph Nodes Arteries Veins | 通用医学数据集 | CT | 胸部血管分割 | Thoracic vascular structures: lymph nodes, arteries, veins segmentation | Hugging Face | https://huggingface.co/datasets/andreped/LyNoS | 访问 | |
| MESA Multi-Ethnic Study of Atherosclerosis | 通用医学数据集 | 动脉粥样硬化研究 | 6,500+ participants, CT/MRI, multi-ethnic atherosclerosis study | MESA | https://www.mesa-nhlbi.org/ | 访问 | ||
| Camelyon Lymph Node Metastasis | 通用医学数据集 | 病理图像 | 转移瘤检测 | 1,000 whole-slide images, lymph node metastasis detection | Camelyon | https://camelyon17.grand-challenge.org/ | 访问 | |
| German National Cohort Whole-Body MRI | 通用医学数据集 | MRI | 人群队列成像 | Whole-body MR imaging, 30,000 subjects planned, German National Cohort | GNC | https://nako.de/en/ | 访问 |