Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection.
Comput. Biol. Medicine, 2025
Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge.
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IEEE Trans. Medical Imaging, August, 2024
Structure and position-aware graph neural network for airway labeling.
Medical Image Anal., 2024
Optimization of Approximate Maps for Linear Systems Arising in Discretized PDEs.
CoRR, 2024
Towards automatic forecasting of lung nodule diameter with tabular data and CT imaging.
Biomed. Signal Process. Control., 2024
Dataset for: Kidney abnormality segmentation in thorax-abdomen CT scans.
Dataset, June, 2023
Dense regression activation maps for lesion segmentation in CT scans of COVID-19 patients.
Medical Image Anal., May, 2023
Transfer learning from a sparsely annotated dataset of 3D medical images.
CoRR, 2023
Kidney abnormality segmentation in thorax-abdomen CT scans.
CoRR, 2023
Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks.
CoRR, 2023
Reproducibility of Training Deep Learning Models for Medical Image Analysis.
Proceedings of the Medical Imaging with Deep Learning, 2023
Automated COVID-19 Grading With Convolutional Neural Networks in Computed Tomography Scans: A Systematic Comparison.
IEEE Trans. Artif. Intell., 2022
Exploring the interpretability of deep neural networks used for gravitational lens finding with a sensitivity probe.
Astron. Comput., 2022
Deep Clustering Activation Maps for Emphysema Subtyping.
CoRR, 2021
Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients.
CoRR, 2021
What happened here? Children integrate physical reasoning to infer actions from indirect evidence.
Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, 2021
Surveying the reach and maturity of machine learning and artificial intelligence in astronomy.
WIREs Data Mining Knowl. Discov., 2020
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans.
IEEE Trans. Medical Imaging, 2020
"Sensie": Probing the sensitivity of neural networks.
J. Open Source Softw., 2020
Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study.
CoRR, 2020
Contextual Two-Stage U-Nets for Robust Pulmonary Lobe Segmentation in CT Scans of COVID-19 and COPD Patients.
CoRR, 2020
Feasibility of end-to-end trainable two-stage U-Net for detection of axillary lymph nodes in contrast-enhanced CT based on sparse annotations.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network.
CoRR, 2018
Towards an Automatic Lung Cancer Screening System in Low Dose Computed Tomography.
Proceedings of the Image Analysis for Moving Organ, Breast, and Thoracic Images, 2018
Organ detection in thorax abdomen CT using multi-label convolutional neural networks.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks.
IEEE Trans. Medical Imaging, 2016
Towards automatic pulmonary nodule management in lung cancer screening with deep learning.
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CoRR, 2016
Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images.
IEEE Trans. Medical Imaging, 2015
Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model.
Proceedings of the Medical Imaging 2015: Computer-Aided Diagnosis, 2015
Automatic detection of spiculation of pulmonary nodules in computed tomography images.
Proceedings of the Medical Imaging 2015: Computer-Aided Diagnosis, 2015
Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015
Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images.
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Medical Image Anal., 2014
Automated detection and quantification of micronodules in thoracic CT scans to identify subjects at risk for silicosis.
Proceedings of the Medical Imaging 2014: Computer-Aided Diagnosis, San Diego, 2014
Computer-Aided Detection of Ground Glass Nodules in Thoracic CT Images Using Shape, Intensity and Context Features.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011, 2011