Search Results for author:
Found 14 papers, 5 papers with code
Nodule detection and generation on chest X-rays: NODE21 Challenge
To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays.
Transfer learning from a sparsely annotated dataset of 3D medical images
A base segmentation model (3D U-Net) was trained on a large and sparsely annotated dataset; its weights were used for transfer learning on four new down-stream segmentation tasks for which a fully annotated dataset was available.
Kidney abnormality segmentation in thorax-abdomen CT scans
Our end-to-end segmentation method was trained on 215 contrast-enhanced thoracic-abdominal CT scans, with half of these scans containing one or more abnormalities.
Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks
Manual analysis of emphysema subtypes and severity is laborious and subjective.
Structure and position-aware graph neural network for airway labeling
The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks (CNN) and enriched using graph neural networks.
Deep Clustering Activation Maps for Emphysema Subtyping
We propose a deep learning clustering method that exploits dense features from a segmentation network for emphysema subtyping from computed tomography (CT) scans.
Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients
Therefore, we propose a weakly-supervised segmentation method based on dense regression activation maps (dRAMs).
Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study
This paper identifies a variety of components that increase the performance of CNN-based algorithms for COVID-19 grading from CT images.
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans
We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images.
Finding strong lenses in CFHTLS using convolutional neural networks
An ensemble of trained networks was applied to all of the 171 square degrees of the CFHTLS wide field image data, identifying 18, 861 candidates including 63 known and 139 other potential lens candidates.
Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set.
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy.
