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数据资源abdominal CT and MRI with multi-organ annotationsabdominal multi-organ segmentation benchmarkAMOS 2022 challenge benchmark; see official Grand Challenge page申请访问

AMOS 腹部多器官分割基准

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.

医学影像计算数据集abdominal imagingmulti-organ segmentationCTMRI查看数据资源
数据资源retinal fundus photographs with glaucoma and structure annotationsophthalmology fundus image challenge datasetREFUGE challenge dataset; official splits described on Grand Challenge申请访问

REFUGE 视网膜眼底青光眼挑战数据集

REFUGE is a retinal fundus imaging challenge dataset for glaucoma assessment. It supports glaucoma classification, optic disc and cup segmentation, fovea localization, and fair comparison of ophthalmology AI methods on color fundus photographs.

医学影像计算数据集ophthalmologyfundusglaucoma分割查看数据资源
数据资源histopathology whole-slide imagesdigital pathology whole-slide image datasetCAMELYON17 challenge dataset; see Grand Challenge page申请访问

CAMELYON17 组织病理淋巴结转移数据集

CAMELYON17 is a digital pathology dataset for detecting breast cancer metastases in lymph node whole-slide images across multiple centers. It supports pathology classification, metastasis detection, weakly supervised learning, and domain generalization in histopathology AI.

医学影像计算数据集pathologywhole-slide imagingbreast cancerdomain generalization查看数据资源
技术竞赛Open soonperipelvic fracture segmentation and reduction planningpelvic fracture CT imaging截止 北京时间 2026-08-19

骨盆周围骨折分割与复位规划挑战

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. Peripelvic fractures are severe injuries with high disability and mortality rates. The PENGWIN 2026 Challenge aims to advance state-of-the-art techniques for intelligent surgical planning in 3D CT scans. It consists of three tasks: fully automated peripelvic fracture segmentation (Task 1), interactive segmentation (Task 2), and fracture reduction planning (Task 3). The dataset features 500 clinical cases with expert annotations and 16,000 simulated fracture cases to support the training of data-driven reduction models. Start date: 2026-05-10. End/deadline date: 2026-08-19.

医学影像计算竞赛Grand Challenge 平台PENGWIN2026OPEN_SOONperipelvic fracture segmentation and reduction planning查看竞赛详情
技术竞赛Open soonhead and neck tumor lesion segmentation, staging, and prognosishead and neck oncology imaging截止 北京时间 2026-07-24

HECKTOR：头颈部肿瘤病灶分割、分期与预后挑战

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. HEad and neCK TumOR Lesion Segmentation, Staging and Prognosis Start date: 2026-05-31. End/deadline date: 2026-07-24.

医学影像计算竞赛Grand Challenge 平台HECKTOR26OPEN_SOONhead and neck tumor lesion segmentation, staging, and prognosis查看竞赛详情
技术竞赛Openbreast cancer histopathology semantic segmentationH&E whole-slide histopathology images开始 北京时间 2026-05-03

BEETLE 乳腺癌组织病理分割挑战

Grand Challenge official API lists this medical AI challenge with status OPEN. BEETLE is a multicenter, multiscanner benchmark for breast cancer histopathology segmentation. It focuses on multiclass semantic segmentation of hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) into four tissue categories: invasive epithelium, non-invasive epithelium, necrosis, and other. The evaluation set comprises 170 densely annotated regions from 54 WSIs, covering all molecular subtypes and histological grades, thereby capturing much of the morphological heterogeneity seen in clinical practice. BEETLE provides a standardized resource for benchmarking breast cancer segmentation models, supporting the development of robust, generalizable algorithms for large-scale biomarke...

医学影像计算竞赛Grand Challenge 平台BEETLEOPENbreast cancer histopathology semantic segmentation查看竞赛详情