output
20102026
most citedThe role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies

818 citations

Showing 2017 · cs.CVShow all

7 papers · 2 filters

cs.CV2017

Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing

Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen +1

Segmenting tree structures is common in several image processing applications. In medical image analysis, reliable segmentations of airways, vessels, neurons and other tree structu…

cs.CV2017★ 18 cited

Early Experiences with Crowdsourcing Airway Annotations in Chest CT

Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo +2

Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine le…

cs.CV2017★ 2 cited

Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks

Gerda Bortsova, Gijs van Tulder, Florian Dubost +5

Intracranial carotid artery calcification (ICAC) is a major risk factor for stroke, and might contribute to dementia and cognitive decline. Reliance on time-consuming manual annota…

cs.CV2017★ 27 cited

Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images

Isabel Pino Peña, Veronika Cheplygina, Sofia Paschaloudi +5

A method for automatically quantifying emphysema regions using High-Resolution Computed Tomography (HRCT) scans of patients with chronic obstructive pulmonary disease (COPD) that d…

cs.CV2017

GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network

Florian Dubost, Gerda Bortsova, Hieab Adams +4

We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a…

cs.CV2017★ 30 cited

Label Stability in Multiple Instance Learning

Veronika Cheplygina, Lauge Sørensen, David M. J. Tax +2

We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers. These classifiers are trained only on globally annotated images (bags), b…