Table 3.
Key features of solution method.
| Solution method | Key features | Advantages | Disadvantages |
|---|---|---|---|
| UNet | Biomedical image segmentation | Excellent on small medical datasets | May overfit on small datasets |
| CNN | Visual data analysis | Good performance on large, labeled image datasets | Requires large amounts of data and computational resources |
| 3D-CNN | 3D spatial relationships | Superior on 3D medical imaging datasets | Requires larger computational resources and data |
| Transformer | Self-attention mechanisms | Handles long-range dependencies, parallelizable | Computationally intensive, needs tuning |
| GAN | Data generation | Augments existing data, improves model robustness | Training can be unstable and difficult |
| Model Ensembling | Combines multiple models | Leverages strengths of each model, improves performance | Increases computational complexity |
| Supervised | Learns from labeled data | High performance on large labeled datasets | Requires labeled data, expensive to collect |
| Self-supervised | Creates learning task from data itself, such as Masked Image Modeling | Efficient use of data, learns better feature representations, supports pre-training and fine-tuning | Performance may be lower than supervised methods |
| Unsupervised | Learns from unlabeled data, such as K-means | No need for labels, discovers unknown patterns, suitable for anomaly detection | Learned features may not be task-specific |
| Transfer learning | Uses pre-trained model | Reduces need for data and computational resources | Pre-trained model may require adjustments |
| Incremental learning | Gradual learning over time | Adapts to new data over time, less memory-intensive | Sensitive to data order, may forget old data |
| Federated Learning | Trains across multiple decentralized devices | Preserves privacy, learns from distributed data | Requires careful coordination, faces data heterogeneity issues |
| Bayesian | Provides measure of uncertainty | Important in medical applications for risk assessment | Computationally intensive, needs careful design of prior |
| Fourier | Transforms data into different domain | Reveals periodic patterns, filters noise | May lose spatial information |
| Logistic regression | Used for binary classification tasks | Simple, fast, interpretable results | May struggle with complex tasks |
| Data augmentation | Increases amount of training data | Improves model performance and robustness | Augmented data may not cover all possible variations |
| Normalization | Adjusts values to a common scale | Improves performance, reduces influence of outliers | May lose information about original scale |
| FLAIR | High-contrast images | Suppression of cerebrospinal fluid signals | Sensitive to magnetic field inhomogeneities |
| iBEAT V2.0 | Comprehensive processing and analysis of brain MRI data | User-friendly interface, comprehensive solution | Requires substantial computational resources, steep learning curve |
| FreeSurfer | Comprehensive processing and analyzing of brain MRI data | High-quality cortical surface reconstructions, quantification of brain structures | Long execution time, steep learning curve |
| LST | Automatic segmentation | handling multi-modal MRI data | Performance influenced by image quality and lesion type |