Omkar2118/datasets huggingface dataset:
CAMELYON16
Full name: Cancer Metastases in Lymph Nodes Challenge 2016

The dataset consists of 400 whole-slide images (WSIs) of lymph node sections stained with hematoxylin and eosin (H&E), collected from two medical centers in the Netherlands. The WSIs are stored in a multi-resolution pyramid format, allowing for efficient retrieval of image subregions at different magnification levels. The training set includes two subsets: 

- 170 WSIs (100 normal, 70 with metastases) from Radboud University Medical Center
- 100 WSIs (60 normal, 40 with metastases) from University Medical Center Utrecht

The test set consists of 130 WSIs from both institutions. Ground truth data for metastases is provided as XML files with annotated contours and WSI binary masks.


The Camelyon16 dataset aims to reduce the workload and subjectivity in cancer diagnosis by pathologists. It serves as a benchmark for evaluating algorithms that can automatically detect metastases in histopathological images, focusing on breast cancer in sentinel lymph nodes.


Researchers can develop and refine machine learning models for automated detection of metastases. The dataset allows for performance comparisons of different detection algorithms. Automated systems can be integrated into clinical workflows to enhance diagnostic accuracy and efficiency. The dataset is valuable for training medical professionals in digital pathology and AI applications in diagnostics.

Introducing paper: Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
FineDiving

We construct a fine-grained video dataset organized by both semantic and temporal structures, where each structure contains two-level annotations.

   * For semantic structure, the action-level labels describe the action types of athletes and the step-level labels depict the sub-action types of consecutive steps in the procedure, where adjacent steps in each action procedure belong to different sub-action types. A combination of sub-action types produces an action type.

   * In temporal structure, the action-level labels locate the temporal boundary of a complete action instance performed by an athlete. During this annotation process, we discard all the incomplete action instances and filter out the slow playbacks. The step-level labels are the starting frames of consecutive steps in the action procedure.

Introducing paper: FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment
AgentCourt

550cases

Introducing paper: AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents