## 1 Introduction  
Spontaneous intracerebral hemorrhage (ICH), or hemorrhagic stroke, is the most disabling form of stroke with a 40% mortality rate and 80% of survivors failing to regain functional independence [1]. The expansion of the initial hematoma volume affects more than one-third of patients and is associated with worse outcomes and increased mortality [2; 3; 4; 5; 6; 7]. Identification of ICH patients at risk of hematoma expansion (HE) as a modifiable risk factor can guide targeted interventions, such as intensive blood pressure reduction, administration of hemostatic agents, or surgical evacuation of small hematomas at risk of expansion [8]. HE is commonly defined by binarizing the ICH volume increase from baseline to follow-up scans [9; 10; 11; 12]. Increasing the expansion threshold--for example, from \(\geq\)3 mL to \(\geq\)6 mL or \(\geq\)12.5 mL HE--is associated with higher specificity and positive predictive value for poor outcomes and mortality [13]. Quantifying hematoma volumes on baseline and follow-up brain scans is the first step in identifying which ICH patients had HE. Manual segmentation of hematomas on brain scans by expert reviewers remains the gold standard for accurate volume quantification and determination of the ground truth in HE classification. However, this process is time-consuming, labor-intensive, and prone to inter-observer variability, making it impractical for large-scale data analysis. To address these challenges, automated segmentation tools are required to streamline the ICH segmentation process and HE classification, facilitating faster, more consistent, and scalable analysis of large ICH datasets. Head CT scans are widely available, fast, and highly sensitive for acute brain hemorrhage and thus are the first line of imaging in an emergency setting when ICH is suspected. Many research groups have developed automated models for segmentation of ICH on head CT scans [14; 15; 16; 17; 18; 19; 20]. These models usually use U-shaped convolutional neural networks (CNNs) to automatically identify and delineate hemorrhagic lesions on head CT. Although automated ICH segmentation tools in head CTs are highly accurate [16; 17; 18; 19; 20], they are still subject to a degree of error. When quantifying hematoma volumes in two separate scans, such as baseline and follow-up head CTs, even small segmentation errors can accumulate. This compounding of errors affects the accurate quantification of hematoma volume changes from baseline to follow-up scan, leading to potential HE misclassification. A practical approach for accurate classification of HE in large datasets is to focus expert visual review only on subjects where cumulative errors in the segmentation of baseline and follow-up hematoma could be large enough to cause misclassification. Mitigating cumulative errors from hematoma segmentation on baseline and follow-up scans for HE classification can be challenging, since different factors may introduce biases in segmentations of hematomas in these two separate scans. Correcting these errors based solely on extracted volume measurements may be inadequate, as volume data alone lack direct contextual information about the original scans. To address this issue, we propose a deep learning model that integrates segmentation masks with the original scans to directly classify each subject's HE status. Recognizing the difficulty of achieving absolute accuracy, we suggest using a high-sensitivity threshold for HE classification to minimize false-negative rates while limiting the number of test-positive cases requiring expert review of segmentation. In such a scenario, a highly sensitive model can accurately identify nearly all subjects with HE while reliably excluding those without HE, limiting the need for expert review to test-positive cases to correct potential false-positive instances. As shown in the example in Figure 1, by limiting expert review to approximately one-third of the dataset (35.5%), we achieved over 99% accuracy in HE classification, with a final false-negative rate of only 0.79%.