Develop models of clinical image data

ID: B2AI_USECASE:25

Name: Develop models of clinical image data.

Description: Developing models of clinical image data may involve annotation, preprocessing, and model training. Generally, annotation requires labeling images with disease or clinical phenotype-relevant information such as labels, bounding boxes, and segmentation masks. The annotation process may be assisted by automated methods, particularly in cases where patient features are already known. Preprocessing such as resizing, normalization, and data augmentation then prepares the labeled images for model training. The training process applies machine learning algorithms to learn patterns from the data and make predictions on new images.

Category: modeling

Involved in: Experimental Design, Metadata Management

Data Topics:

Enables:

  • B2AI_USECASE:30 (Test and deploy analytical models of clinical image data.)

Enabled by:

  • B2AI_USECASE:19 (Standardize clinical image data collected from multiple sites and sources.)

Relevant to GCs:

  • B2AI_ORG:115

Standards and Tools:

Contributor: Harry Caufield (ORCID:0000-0001-5705-7831)