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Top AI Model Prediction Results
Data Science and Analytics
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Explores public submission outputs generated during the SIIM-ISIC Melanoma Classification challenge. It provides crucial results and prediction probabilities derived from various highly optimised machine learning models, including those utilising architectures like InceptionResnetV2, EfficientNetB3, and Seresnext50. This data is essential for model benchmarking, analysis of predictive accuracy, and studying how different solutions approach the diagnosis of malignant skin lesions. It offers insight into high-performing strategies used in large-scale computer vision competitions focused on health outcomes.
Columns
image_name: The unique identifier for the image, corresponding to the original image name used in the challenge. This column is fully populated with distinct values.target: The output score or prediction probability generated by the underlying model, indicating the likelihood of the image representing a melanoma case. Scores range from 0.00 to 0.56 in the primary sample.
Distribution
The data is typically structured for delivery in a CSV file format. A representative sample file is approximately 273.95 kB in size and contains 2 fields. The set holds approximately 11.0 thousand validated records, with no missing entries observed in the sample data for either column.
Usage
- Model evaluation and benchmarking against established public solutions.
- Advanced analysis of ensemble methods, blending, and stacking prediction probabilities.
- Research into variability and robustness of AI-driven diagnostic scores.
- Development and testing of next-generation computer vision algorithms for healthcare.
Coverage
The data coverage is focused purely on model outputs and prediction probabilities for diagnostic imaging related to skin lesions (melanoma classification). As this is derived output, it does not contain new clinical images. The context relates to the SIIM-ISIC competition, covering health and cancer data, but specific geographic or temporal details of the original patients are not included.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: To refine loss functions and evaluate model robustness against high-scoring public benchmarks.
- Machine Learning Engineers: To study the inference pipeline efficiencies of top-performing Kaggle solutions.
- Healthcare Researchers: For investigating the applicability and generalizability of deep learning models in dermatology.
Dataset Name Suggestions
- SIIM-ISIC Public Solution Outputs
- Melanoma Classification Submission Scores
- Top AI Model Prediction Results
- ISIC Competition Benchmarking Scores
Attributes
Original Data Source: Top AI Model Prediction Results
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