[HTML][HTML] Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection
Background and objective: Intracranial hemorrhage (ICH) is a life-threatening emergency
that can lead to brain damage or death, with high rates of mortality and morbidity. The fast …
that can lead to brain damage or death, with high rates of mortality and morbidity. The fast …
Data extrapolation from learned prior images for truncation correction in computed tomography
Data truncation is a common problem in computed tomography (CT). Truncation causes
cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the …
cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the …
Next-generation personalized cranioplasty treatment
Decompressive craniectomy (DC) is a surgical procedure, that is followed by cranioplasty
surgery. DC is usually performed to treat patients with traumatic brain injury, intracranial …
surgery. DC is usually performed to treat patients with traumatic brain injury, intracranial …
A transformer-based network for anisotropic 3D medical image segmentation
D Guo, D Terzopoulos - 2020 25th International Conference on …, 2021 - ieeexplore.ieee.org
Imaging anisotropy poses a critical challenge in applying deep learning models to 3D
medical image analysis. Anisotropy downgrades model performance, especially when slice …
medical image analysis. Anisotropy downgrades model performance, especially when slice …
Bio-inspired feature selection in brain disease detection via an improved sparrow search algorithm
W Yu, H Kang, G Sun, S Liang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The timely diagnosis and treatment of brain diseases have always been an essential part of
saving patients with encephalopathy. At the same time, medical image analysis is a …
saving patients with encephalopathy. At the same time, medical image analysis is a …
Cross-grained contrastive representation for unsupervised lesion segmentation in medical images
Automatic segmentation of lesions in medical images plays a crucial role in the quantitative
assessment of disease progression. While supervised deep learning-based methods have …
assessment of disease progression. While supervised deep learning-based methods have …
Studierfenster: an open science cloud-based medical imaging analysis platform
Imaging modalities such as computed tomography (CT) and magnetic resonance imaging
(MRI) are widely used in diagnostics, clinical studies, and treatment planning. Automatic …
(MRI) are widely used in diagnostics, clinical studies, and treatment planning. Automatic …
An end-to-end approach to combine attention feature extraction and Gaussian Process models for deep multiple instance learning in CT hemorrhage detection
Intracranial hemorrhage (ICH) is a serious life-threatening emergency caused by blood
leakage inside the brain. Radiologists usually confirm the presence of ICH by analyzing …
leakage inside the brain. Radiologists usually confirm the presence of ICH by analyzing …
Segmentation of intracranial hemorrhage using semi-supervised multi-task attention-based U-net
Intracranial Hemorrhage (ICH) has high rates of mortality, and risk factors associated with it
are sometimes nearly impossible to avoid. Previous techniques to detect ICH using machine …
are sometimes nearly impossible to avoid. Previous techniques to detect ICH using machine …
A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection
Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …