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Found 10 papers, 2 papers with code
Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach
A \gls{lstm} trained on the soft cross-entropy outperformed conventional sleep detection algorithms, other neural network architectures and loss functions in accuracy and model calibration.
VISEM-Tracking, a human spermatozoa tracking dataset
A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view.
Principal Component Analysis based frameworks for efficient missing data imputation algorithms
Missing data is a commonly occurring problem in practice.
Artificial Intelligence in Dry Eye Disease
Although the term `AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes.
SinGAN-Seg: Synthetic training data generation for medical image segmentation
The pipeline is evaluated using qualitative and quantitative comparisons between real and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited.
Dyadic aggregated autoregressive (DASAR) model for time-frequency representation of biomedical signals
DASAR can provide a more accurate representation of the (highly contrasted) EEG and fNIRS frequency ranges by increasing the estimation accuracy in user-defined spectrum region of interest (SROI).
The Complex-Pole Filter Representation (COFRE) for spectral modeling of fNIRS signals
The complex-pole frequency representation (COFRE) is introduced in this paper as a new approach for spectrum modeling in biomedical signals.
Efficient Quantile Tracking Using an Oracle
The MSE is decomposed in tracking variance and bias and novel and efficient procedures to estimate these quantities are presented.
A hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy
In the current paper, we introduce a parametric data-driven model for functional near-infrared spectroscopy that decomposes a signal into a series of independent, rescaled, time-shifted, hemodynamic basis functions.
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
In the field of male human reproduction, clinical and biological data are not used to its fullest potential.
