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Found 10 papers, 3 papers with code
CenTime: Event-Conditional Modelling of Censoring in Survival Analysis
We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data.
A data-centric deep learning approach to airway segmentation
In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity.
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study
The Cox neural network can achieve an IPCW C-index of 0. 75 on the internal dataset and 0. 69 on an external dataset.
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease.
Optimizing Short-Time Fourier Transform Parameters via Gradient Descent
The Short-Time Fourier Transform (STFT) has been a staple of signal processing, often being the first step for many audio tasks.
Domain-Adaptive Few-Shot Learning
Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples.
Face-Focused Cross-Stream Network for Deception Detection in Videos
To address the training data scarcity problem, our FFCSN model is trained with both meta learning and adversarial learning.
Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning
Specifically, we assume that each synthesised data point can belong to any unseen class; and the most likely two class candidates are exploited to learn a robust projection function in a competitive fashion.
Domain-Invariant Projection Learning for Zero-Shot Recognition
This is made possible by learning a projection between a feature space and a semantic space (e. g. attribute space).
Generating Nontrivial Melodies for Music as a Service
A melody is then generated by feeding a random sample from that space to the autoencoder's decoder, along with the chord progression generated by the grammar.
