Generative adversarial network in medical imaging: A review
Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …
community due to their capability of data generation without explicitly modelling the …
Data augmentation for brain-tumor segmentation: a review
Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
[HTML][HTML] Application of artificial intelligence in 3D printing physical organ models
Artificial intelligence (AI) and 3D printing will become technologies that profoundly impact
humanity. 3D printing of patient-specific organ models is expected to replace animal …
humanity. 3D printing of patient-specific organ models is expected to replace animal …
Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels
Background Accurate segmentation of brain tumour in magnetic resonance images (MRI) is
a difficult task due to various tumour types. Using information and features from multimodal …
a difficult task due to various tumour types. Using information and features from multimodal …
ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI
Performance of models highly depend not only on the used algorithm but also the data set it
was applied to. This makes the comparison of newly developed tools to previously …
was applied to. This makes the comparison of newly developed tools to previously …
Predicting infarct core from computed tomography perfusion in acute ischemia with machine learning: Lessons from the ISLES challenge
Background and Purpose: The ISLES challenge (Ischemic Stroke Lesion Segmentation)
enables globally diverse teams to compete to develop advanced tools for stroke lesion …
enables globally diverse teams to compete to develop advanced tools for stroke lesion …
A sequential machine learning-cum-attention mechanism for effective segmentation of brain tumor
Magnetic resonance imaging is the most generally utilized imaging methodology that
permits radiologists to look inside the cerebrum using radio waves and magnets for tumor …
permits radiologists to look inside the cerebrum using radio waves and magnets for tumor …
nnsam: Plug-and-play segment anything model improves nnunet performance
Automatic segmentation of medical images is crucial in modern clinical workflows. The
Segment Anything Model (SAM) has emerged as a versatile tool for image segmentation …
Segment Anything Model (SAM) has emerged as a versatile tool for image segmentation …
[HTML][HTML] Inter-rater agreement in glioma segmentations on longitudinal MRI
Background Tumor segmentation of glioma on MRI is a technique to monitor, quantify and
report disease progression. Manual MRI segmentation is the gold standard but very labor …
report disease progression. Manual MRI segmentation is the gold standard but very labor …
Federated learning for medical imaging radiology
Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …