## 4 Discussion
Overall, we demonstrated that for HE classification, high-sensitivity thresholds were more effective at minimizing false-negative rates while excluding a larger proportion of test-negative subjects from expert review, compared to high-specificity thresholds, which resulted in a higher false-positive rate and excluded a smaller proportion of (test-positive) subjects from automated segmentation review. From a clinical standpoint, high-sensitivity thresholds are crucial to ensure the model identifies the majority of patients with HE, thereby minimizing the risk of missing cases with expansion. In practice, there is often a preference to over-diagnose rather than under-diagnose individuals at risk of life-threatening conditions such as HE. High-sensitivity models are also well-suited to serve as components within larger, multimodal risk stratification systems. While our primary objective was to generate ground truth HE labels for a large ICH dataset, rather than to identify high-risk individuals from an admission head CT, accurately identifying and labeling patients with HE remains essential. Figure 1 illustrates how our workflow achieved > 99% accurate classification across the entire cohort, with >95% of HE cases correctly labeled. Importantly, expert reviewers only need to review and correct segmentations in approximately one-third of the dataset, significantly improving efficiency while maintaining accuracy. The combination of CNN and SVM has many advantages [62; 63; 64]. The CNN classification models are specialized for image feature extraction and learn hierarchical features, capturing both low-level and high-level patterns from images. SVMs are specialized to maximize the margin between classes, which often leads to more effective and sharper decision boundaries compared to softmax-based approaches in the final layer of CNN classification models [65; 66]. SVMs are also well-suited for handling high-dimensional feature spaces and can offer better generalization as a classifier on top of a CNN's feature extraction layers, potentially improving the model's robustness to overfitting [36; 37]. The main advantage of using a CNN + SVM over a CNN model was maintaining sensitivity consistency across training and testing datasets. A CNN classifier directly outputs probabilities via a 4-sigmoid activation, which is trained to optimize by loss functions. A sensitivity threshold is applied to these probabilities. However, CNN predictions are closely tied to the training datasets, and imbalanced datasets can skew the probability distribution, leading to suboptimal threshold generalization. The CNN in our approach acts as a feature extractor, and the extracted features are fed into an SVM classifier. The SVM learns decision boundaries in the feature space instead of predicting probabilities directly. Probabilities are inferred later, but SVM decision boundaries are typically more robust to imbalanced data, improving generalization across datasets. SVM-based probabilities are typically more heavily calibrated than the raw sigmoid output of the CNN, leading to better threshold selection across datasets. Since the probability distribution of a CNN is not consistent across datasets, the CNN + SVM approach translates the features extracted from the CNN into robust decision boundaries, making it less susceptible to dataset-specific variations in threshold behavior. In our study, we adopted absolute volume increase thresholds for the definition of HE. Using relative rather than absolute volume increases to define HE can introduce marked variability due to its dependence on the initial hematoma volume. For instance, in the case of a small initial hematoma volume, such as 1 mL, a 33% increase--proposed by some as a threshold for binary classification [13]--results in a cutoff volume of 1.33 mL. This means that a follow-up hematoma measuring 1.4 mL would be classified as having expanded, while a hematoma measuring 1.3 mL would not, despite the absolute difference being only 0.1 mL.