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Found 6 papers, 3 papers with code
H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression
Despite several methods that were developed, stain colour heterogeneity is still an unsolved challenge that limits the development of CNNs that can generalize on data from several medical centers.
Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization
Here we show that our architecture, by learning end-to-end an uncertainty-based weighting combination of multi-task and adversarial losses, is encouraged to focus on pathology features such as density and pleomorphism of nuclei, e. g. variations in size and appearance, while discarding misleading features such as staining differences.
Learning by stochastic serializations
In this paper, we propose to map any complex structure onto a generic form, called serialization, over which we can apply any sequence-based density estimator.
Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks
We present a new method for forecasting systems of multiple interrelated time series.
Space-Time Local Embeddings
We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space.
Two-Stage Metric Learning
This induces in the input data space a new family of distance metric with unique properties.
