Search Results for author:
Found 7 papers, 2 papers with code
Focused Active Learning for Histopathological Image Classification
The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value.
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images
We show that our model achieves better results than other state-of-the-art probabilistic MIL methods.
Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation
It captures the labeling behavior of each rater with a multidimensional probability distribution and integrates this information with the feature maps of the image to produce probabilistic segmentation predictions.
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection
Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown.
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning
Moreover, its probabilistic nature guarantees robustness to overfitting on small datasets and uncertainty estimations for the predictions.
Deep Gaussian Processes for geophysical parameter retrieval
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval.
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting.
