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Explainable machine-learning predictions for the prevention of hypoxaemia during surgery.
Nature biomedical engineering
Oct 10, 2018
Although anaesthesiologists strive to avoid hypoxemia during surgery, reliably predicting future intraoperative hypoxemia is not currently possible. Here, we report the development and testing of a machine-learning-based system that, in real time dur...
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Temporal separation of Cerenkov radiation and scintillation using artificial neural networks in Clinical LINACs.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Oct 10, 2018
The irradiation of scintillator-fiber optic dosimeters by clinical LINACs results in the measurement of scintillation and Cerenkov radiation. In scintillator-fiber optic dosimetry, the scintillation and Cerenkov radiation responses are separated to d...
Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis.
NeuroImage. Clinical
Oct 10, 2018
Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not eff...
CaSTLe - Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments.
PloS one
Oct 10, 2018
Single-cell RNA sequencing (scRNA-seq) is an emerging technology for profiling the gene expression of thousands of cells at the single cell resolution. Currently, the labeling of cells in an scRNA-seq dataset is performed by manually characterizing c...
Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning.
IEEE transactions on bio-medical engineering
Oct 9, 2018
The performance of an existing Devanagari script (DS) input-based P300 speller with conventional machine learning techniques suffers from low information transfer rate (ITR). This occurs due to its required large size of display, i.e., 8 × 8 row-colu...
A Robust AUC Maximization Framework With Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification.
IEEE transactions on neural networks and learning systems
Oct 9, 2018
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unla...
STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation.
IEEE transactions on neural networks and learning systems
Oct 9, 2018
Accurate segmentation of pelvic organs is important for prostate radiation therapy. Modern radiation therapy starts to use a magnetic resonance image (MRI) as an alternative to computed tomography image because of its superior soft tissue contrast an...
Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning.
IEEE transactions on medical imaging
Oct 9, 2018
The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in ...
Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.
European radiology
Oct 9, 2018
OBJECTIVE: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (te...
Application of fractal theory and fuzzy enhancement in ultrasound image segmentation.
Medical & biological engineering & computing
Oct 9, 2018
The manuscript describes an ultrasound image segmentation technique based on the fractional Brownian motion (FBM) model. Here, the ultrasound images are first enhanced using a fuzzy-based technique, and later the FBM model is employed to obtain the f...
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