Search Results for author:
Found 19 papers, 5 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.
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
To reduce these artifacts and enhance the perceptual quality of the results, in this paper, we propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process.
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images
We achieved 0. 996 and 0. 938 F1 scores for blur and folded tissue detection on unseen data, respectively.
Ranked #1 on
Artifact Detection
on HistoArtifacts
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.
Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection
It is, however, essential to be able to understand these new models from the perspective of the experience attained from years of evaluating network security data for anomaly detection.
Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection
The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies.
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion
Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations.
Activation-level uncertainty in deep neural networks
Current approaches for uncertainty estimation in deep learning often produce too confident results.
Deep Gaussian Processes for geophysical parameter retrieval
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval.
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
We apply our method on the problem of fine-tuning for unseen image formation models and on removal of artifacts introduced by GANs.
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting.
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions.
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
Finally, we show that our proposed model, the VSRResFeatGAN model, outperforms current state-of-the-art SR models, both quantitatively and qualitatively.
Remote Sensing Image Classification with Large Scale Gaussian Processes
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources.
Robust and Low-Rank Representation for Fast Face Identification with Occlusions
In this paper we propose an iterative method to address the face identification problem with block occlusions.
Sparse Bayesian Methods for Low-Rank Matrix Estimation
Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesting practical applications.
