AIMC Topic: Sensitivity and Specificity

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Classification of Contextual Use of Left Ventricular Ejection Fraction Assessments.

Studies in health technology and informatics
Knowledge of the left ventricular ejection fraction is critical for the optimal care of patients with heart failure. When a document contains multiple ejection fraction assessments, accurate classification of their contextual use is necessary to filt...

Sampling from Determinantal Point Processes for Scalable Manifold Learning.

Information processing in medical imaging : proceedings of the ... conference
High computational costs of manifold learning prohibit its application for large datasets. A common strategy to overcome this problem is to perform dimensionality reduction on selected landmarks and to successively embed the entire dataset with the N...

Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration.

Information processing in medical imaging : proceedings of the ... conference
This paper presents a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. Using the electrodes identified in the post-implantation CT, surgeons r...

Multi-scale Convolutional Neural Networks for Lung Nodule Classification.

Information processing in medical imaging : proceedings of the ... conference
We investigate the problem of diagnostic lung nodule classification using thoracic Computed Tomography (CT) screening. Unlike traditional studies primarily relying on nodule segmentation for regional analysis, we tackle a more challenging problem on ...

Prediction of Longitudinal Development of Infant Cortical Surface Shape Using a 4D Current-Based Learning Framework.

Information processing in medical imaging : proceedings of the ... conference
Understanding the early dynamics of the highly folded human cerebral cortex is still an actively evolving research field teeming with unanswered questions. Longitudinal neuroimaging analysis and modeling have become the new trend to advance research ...

Finding a Path for Segmentation Through Sequential Learning.

Information processing in medical imaging : proceedings of the ... conference
Sequential learning techniques, such as auto-context, that applies the output of an intermediate classifier as contextual features for its subsequent classifier has shown impressive performance for semantic segmentation. We show that these methods ca...

Bodypart Recognition Using Multi-stage Deep Learning.

Information processing in medical imaging : proceedings of the ... conference
Automatic medical image analysis systems often start from identifying the human body part contained in the image; Specifically, given a transversal slice, it is important to know which body part it comes from, namely "slice-based bodypart recognition...

Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks.

Information processing in medical imaging : proceedings of the ... conference
Learning representative computational models from medical imaging data requires large training data sets. Often, voxel-level annotation is unfeasible for sufficient amounts of data. An alternative to manual annotation, is to use the enormous amount o...

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

Information processing in medical imaging : proceedings of the ... conference
Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optim...

Automated Classification of Clinical Incident Types.

Studies in health technology and informatics
We consider the task of automatic classification of clinical incident reports using machine learning methods. Our data consists of 5448 clinical incident reports collected from the Incident Information Management System used by 7 hospitals in the sta...