Scalp Hair Loss Condition Dataset
Main product
Data Quantity
Optional product
Total Price
USD 0 VAT included
- Main productPremium
- Data QuantityBasic
- Optional productNot selected
- DownloadNo data
- All products are priced including VAT.
- Premium datasets are custom-made and take approximately two weeks from the date of application to improve quality.
- Common datasets can be checked on My Page after purchase.
Basic Price
USD 17,400 VAT included
Data on hair loss, which can be used to diagnose and predict hair loss, evaluate treatment effectiveness, and evaluate the key characteristics of hair loss.
- Labeling type: Hair Loss
- Data Format: Image
Related Tags:
Categories:
About Dataset
1) Data introduction
• “Hair Loss Data” provides data on hair loss.2) Data utilization
(1)Hair Loss data has characteristics that: • It is divided into 4 levels ("Normal", "Mild", "Moderate", and "Severe"). (2)Hair Loss data can be used to: • Diagnosis and prediction: Hair loss data can be used to accurately diagnose and predict the type and severity of hair loss through various imaging techniques and analysis. • Evaluation of treatment effectiveness: During the treatment process, hair loss data can be used to help evaluate the effectiveness of treatment and adjust actions if necessary.Meta Data
Normal
Outstanding
Data Samples 5
Utility
| Downstream Classification (▲) | KID (▼) | One Class Classification (▼) | |
|---|---|---|---|
| Total | 0 | 0 | 0 |
| Suitability | OK | OK | OK |
The higher the value, the better (▲)
Model Performance
Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models. Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.
The closer to zero or the lower the value, the better (▼)
Quality
KID (Kernel Inception Distance) is a metric used to evaluate the similarity between generated images and real images. It compares the differences between the two sample distributions using Kernel Mean Embedding, without assuming a normal distribution. Interpretation: A lower KID score suggests that generated images are more similar to real images, with a score close to 0 being ideal. Specifically, a score below 0.01 indicates very high similarity.
Privacy
| LPIPS (▲) | SSIM (▼) | |
|---|---|---|
| Total | 0 | 0 |
| Suitability | OK | OK |
The higher the value, the better (▲)
Perceptual Similarity
Learned Perceptual Image Patch Similarity (LPIPS) is a metric used to measure the visual similarity between two images by utilizing neural networks to extract key features and calculate the distance between them. High LPIPS value: Indicates high similarity between images, raising the risk of information leakage. Low LPIPS value: Suggests that synthetic images are perceptually different from real images, indicating a lower risk of sensitive information leakage.
The closer to zero or the lower the value, the better (▼)
Structural Similarity
The Structural Similarity Index Measure (SSIM) is a metric used to assess the similarity between two images. It is primarily used to compare the quality of a restored or compressed image with the original image. SSIM measures visual similarity by considering brightness, contrast, and structure.
High SSIM value (0.9 or above): Indicates that the synthetic image is very similar to the real image, which may increase the risk of information leakage.
Low SSIM value (0.6 or below): Indicates low similarity and reduced risk of leakage.
Premium Report Information
Premium dataset sample
