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Found 9 papers, 0 papers with code
SHAMANN: Shared Memory Augmented Neural Networks
Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an image-to-image mapping.
ConTrack: Contextual Transformer for Device Tracking in X-ray
Tracking the catheter tip poses different challenges: the tip can be occluded by contrast during angiography or interventional devices; and it is always in continuous movement due to the cardiac and respiratory motions.
Self-supervised Learning from 100 Million Medical Images
Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the creation of large and curated sets of annotated training examples.
Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities.
Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth
Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19.
Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment
In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e. g., by 8% to 0. 91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs.
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks
To make the network more robust to unanticipated noise perturbations, we use PGD to search for noise patterns that can trigger the network to give over-confident mistakes.
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
We argue that explicitly learning the classification uncertainty as an orthogonal measure to the predicted output, is essential to account for the inherent variability characteristic of this data.
Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels
Chest X-ray (CXR) is the most common X-ray examination performed in daily clinical practice for the diagnosis of various heart and lung abnormalities.
