Intracerebral hemorrhage (ICH) has one of the worst prognoses among patients with stroke.
Surgical measures have been adopted to relieve the mass effect of the hematoma, and …

Acute intracerebral hemorrhage (ICH) is a devastating type of stroke worldwide. Neuronal
destruction involved in the brain damage process caused by ICH includes a primary injury …

Purpose In clinical work, accurately measuring the volume and the size of breast cancer is
significant to develop a treatment plan. However, it is time-consuming, and inter-and intra …

Computer-aided diagnosis (CAD) for intracranial hemorrhage (ICH) is needed due to its
high mortality rate and time sensitivity. Training a stable and robust deep learning-based …

This study evaluated deep learning algorithms for semantic segmentation and quantification
of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular …

In recent years, there have been major advances in deep learning algorithms for image
recognition in traumatic brain injury (TBI). Interest in this area has increased due to the …

Objectives To predict the functional outcome of patients with intracerebral hemorrhage (ICH)
using deep learning models based on computed tomography (CT) images. Methods A …

Background Accurate measurement of hemorrhage volume is critical for both the prediction
of prognosis and the selection of appropriate clinical treatment after spontaneous …

Objective Patients with spontaneous intracerebral hemorrhage (ICH) at the highest risk of
hematoma growth are those with the most potential to benefit from anti‐expansion treatment …

The study aims to enhance the accuracy and practicability of CT image segmentation and
volume measurement of ICH by using deep learning technology. A dataset including the …