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SIIM COVID-19 Competition Solutions List
Patient Health Records & Digital Health
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About
This database contains the specifications and performance metrics for competitive machine learning solutions developed during the SIIM COVID-19 Detection challenge. It is designed to provide quick access to successful methodologies, team names, corresponding Kaggle notebook links, and the final scores achieved by each group until August 11. The data facilitates the analysis of effective deep learning strategies in computational pathology.
Columns
- Team Name: The name of the team or individual submitting the solution. There are 47 distinct entries recorded.
- Notebook: A direct link to the solution notebook hosted on Kaggle, allowing immediate review of the model implementation.
- Score: The final performance score achieved by the solution in the competition. Scores in this collection range from a minimum of 0.09 to a maximum of 0.64, with a mean score of 0.38.
Distribution
The data is provided in a CSV file format named
solutions.csv, with a total size of 3.97 kB. It is structured with 3 columns and contains 47 records, each representing a unique solution entry. The dataset is a fixed snapshot, and the expected update frequency is never.Usage
This data is ideal for benchmarking high-scoring computer vision models and analysing the competitive landscape of medical imaging challenges. It is useful for data scientists seeking to understand performance distribution, identify winning strategies, and quickly locate validated code implementations for COVID-19 detection tasks.
Coverage
The coverage pertains exclusively to the competitive solutions submitted for the SIIM COVID-19 Detection contest, specifically tracking those published up until August 11. Since the data relates to digital competition entries, there is no geographic or demographic scope associated with this product.
License
CC0: Public Domain
Who Can Use It
Data scientists, machine learning researchers, and deep learning engineers looking for practical examples of successful medical imaging models. Academic researchers studying competitive data science dynamics. Competition organisers seeking detailed post-event analysis of participant results and methodology sharing.
Dataset Name Suggestions
- SIIM COVID-19 Competition Solutions List
- SIIM Detection Model Scorecard
- Kaggle SIIM COVID-19 Detection Benchmarks
- Solution Specifications for SIIM COVID-19
Attributes
Original Data Source: SIIM COVID-19 Competition Solutions List
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