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Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study
BackgroundPatients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70–80% of the...
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Neural similarity across task load relates to cognitive reserve and brain maintenance measures on the Letter Sternberg task: a longitudinal study
The aging process is characterized by change across several measures that index cognitive status and brain integrity. In the present study, 54...
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Autism spectrum disorders detection based on multi-task transformer neural network
Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying...
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Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans
BackgroundChest Computed tomography (CT) scans detect lung nodules and assess pulmonary fibrosis. While pulmonary fibrosis indicates increased lung...
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Optimising the diagnostic accuracy of First post-contrAst SubtracTed breast MRI (FAST MRI) through interpretation-training: a multicentre e-learning study, map** the learning curve of NHS Breast Screening Programme (NHSBSP) mammogram readers using an enriched dataset
BackgroundAbbreviated breast MRI (FAST MRI) is being introduced into clinical practice to screen women with mammographically dense breasts or with a...
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Hayling and stroop tests tap dissociable deficits and network-level neural correlates
Although many executive function screens have been developed, it is not yet clear whether these assessments are equally effective in detecting...
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Brief inductions in episodic past or future thinking: effects on episodic detail and problem-solving
Episodic specificity inductions, involving brief training in recollecting episodic details, have been shown to improve subsequent performance on...
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The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke
BackgroundRobots can generate rich kinematic datasets that have the potential to provide far more insight into impairments than standard clinical...
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Evaluating the effectiveness of abbreviated breast MRI (abMRI) interpretation training for mammogram readers: a multi-centre study assessing diagnostic performance, using an enriched dataset
BackgroundAbbreviated breast MRI (abMRI) is being introduced in breast screening trials and clinical practice, particularly for women with dense...
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Abnormal trial-to-trial variability in P300 time-varying directed eeg network of schizophrenia
Cognitive disturbance in identifying, processing, and responding to salient or novel stimuli are typical attributes of schizophrenia (SCH), and P300...
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Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network
COVID-19 is a deadly outbreak that has been declared a public health emergency of international concern. The massive damage of the disease to public...
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A hybrid EEG classification model using layered cascade deep learning architecture
The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional...
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Associating Aversive Task Exposure with Pharmacological Intervention to Model Traumatic Memories in Laboratory Rodents
Post-traumatic stress disorder is associated with highly threatening and stressful events. The underlying memory is overconsolidated, leading to...
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Applications of Nanotechnology in Converging the Biomarker Science for Advancement in Cancer Detection and Treatment
Cancer biomarkers with high selectivity, specificity, and reproducibility assume fundamental task in diagnosis, prognosis, and prediction of therapy...
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Motor sequences; separating the sequence from the motor. A longitudinal rsfMRI study
In motor learning, sequence specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI...
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Feasibility, Contrast Sensitivity and Network Specificity of Language fMRI in Presurgical Evaluation for Epilepsy and Brain Tumor Surgery
Language fMRI has become an integral part of the planning process in brain surgery. However, fMRI may suffer from confounding factors both on the...
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Good view frames from ultrasonography (USG) video containing ONS diameter using state-of-the-art deep learning architectures
This paper presents an automated method for detection of the diagnostically prominent frames containing optic nerve sheath (ONS) from ocular...
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An improved CNN-based architecture for automatic lung nodule classification
Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung...
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Virtual Reality for Motor and Cognitive Rehabilitation
Virtual Reality (VR) affords clinicians the ability to deliver safe, controlled, task-specific customised interventions that are enjoyable,...
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Noninvasive oral cancer screening based on local residual adaptation network using optical coherence tomography
AbstractOral cancer is known as one of the relatively common malignancy types worldwide. Despite the easy access of the oral cavity to examination,...
