187 citations · 451 across the 28 of their papers we have counts for
5 papers · 1 filter
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
Florian Dubost, Erin Hong, Nandita Bhaskhar +3
Labeled data is a critical resource for training and evaluating machine learning models. However, many real-life datasets are only partially labeled. We propose a semi-supervised m…
DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data
Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal +7
Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause s…
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Gerda Bortsova, Cristina González-Gonzalo, Suzanne C. Wetstein +9
Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulner…
Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images
Subhradeep Kayal, Florian Dubost, Harm A. W. M. Tiddens +1
Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present…
When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans
Oliver Werner, Kimberlin M. H. van Wijnen, Wiro J. Niessen +4
To measure the volume of specific image structures, a typical approach is to first segment those structures using a neural network trained on voxel-wise (strong) labels and subsequ…