CVC HDClassif - sporedata/researchdesigneR GitHub Wiki

General description

The CVC-HDClassif dataset is associated with medical imaging, particularly in the context of computer vision and classification tasks related to gastrointestinal (GI) health, often used in the field of colorectal cancer screening or polyp detection. This dataset is most commonly referred to in studies involving high-definition (HD) colonoscopy imagery for classifying various types of abnormalities such as polyps or lesions.

The CVC-HDClassif dataset plays an important role in the development of AI-based systems aimed at colorectal cancer screening, which is a critical area of preventive medicine. Early detection of precancerous polyps through colonoscopy and subsequent removal can significantly reduce the incidence of colorectal cancer. AI models trained on datasets like CVC-HDClassif can serve as decision-support tools for clinicians, helping them to identify polyps more effectively during real-time colonoscopy procedures.

Data Categories

Image Annotations: The dataset includes manually annotated labels that mark regions of interest, such as specific polyps or tissue abnormalities, often by expert endoscopists or pathologists. These annotations allow for supervised training in machine learning models.

High-Definition Colonoscopy Images: The dataset contains HD images or frames captured during colonoscopy procedures. These images may focus on the colon or rectum's internal surfaces, where medical professionals look for abnormalities like polyps, adenomas, or cancerous lesions.

Limitations

1. Class Imbalance: In some cases, there might be fewer images of certain polyp types (e.g., cancerous lesions) compared to others, which can make model training more challenging and require techniques such as oversampling or cost-sensitive learning.

2. Visual Variability: The appearance of polyps can vary significantly in terms of size, shape, and color, which presents challenges in training models to generalize well to new, unseen data.

3. Lighting Conditions: Colonoscopy images can have varying lighting conditions, reflections, and occlusions, which can affect the performance of models if not properly handled during preprocessing.

Related publications

• GTCreator: a flexible annotation tool for image-based datasets

• Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis

Data access

For more information on the CVC-HDClassif dataset, visit https://giana.grand-challenge.org/