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Mathilde M. W. Wille
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2020 – today
- 2020
- [j6]Silas Nyboe Ørting
, Jens Petersen
Learning to Quantify Emphysema Extent: What Labels Do We Need? IEEE J. Biomed. Health Informatics 24(4): 1149-1159 (2020) - [j5]Ruwan B. Tennakoon
Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem. IEEE Trans. Medical Imaging 39(4): 854-865 (2020)
2010 – 2019
- 2018
- [c8]Silas Nyboe Ørting
Detecting emphysema with multiple instance learning. ISBI 2018: 510-513 - [c7]Silas Nyboe Ørting
Feature Learning Based on Visual Similarity Triplets in Medical Image Analysis: A Case Study of Emphysema in Chest CT Scans. CVII-STENT/LABELS@MICCAI 2018: 140-149 - [c6]Gerda Bortsova
Deep Learning from Label Proportions for Emphysema Quantification. MICCAI (2) 2018: 768-776 - [i4]Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne:
Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans. CoRR abs/1806.07131 (2018) - [i3]Gerda Bortsova, Florian Dubost, Silas N. Ørting, Ioannis Katramados, Laurens Hogeweg, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne:
Deep Learning from Label Proportions for Emphysema Quantification. CoRR abs/1807.08601 (2018) - [i2]Silas Nyboe Ørting, Jens Petersen, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne:
Learning to quantify emphysema extent: What labels do we need? CoRR abs/1810.07433 (2018) - 2017
- [c5]Silas Nyboe Ørting
Crowdsourced Emphysema Assessment. CVII-STENT/LABELS@MICCAI 2017: 126-135 - 2016
- [j4]Arnaud Arindra Adiyoso Setio, Francesco Ciompi
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks. IEEE Trans. Medical Imaging 35(5): 1160-1169 (2016) - [i1]Francesco Ciompi, Kaman Chung, Sarah J. van Riel, Arnaud Arindra Adiyoso Setio, Paul K. Gerke, Colin Jacobs, Ernst Th. Scholten, Cornelia Schaefer-Prokop, Mathilde M. W. Wille, Alfonso Marchiano, Ugo Pastorino, Mathias Prokop, Bram van Ginneken:
Towards automatic pulmonary nodule management in lung cancer screening with deep learning. CoRR abs/1610.09157 (2016) - 2015
- [j3]Francesco Ciompi
Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images. IEEE Trans. Medical Imaging 34(4): 962-973 (2015) - [j2]Aasa Feragen
Geodesic Atlas-Based Labeling of Anatomical Trees: Application and Evaluation on Airways Extracted From CT. IEEE Trans. Medical Imaging 34(6): 1212-1226 (2015) - [c4]Arnaud A. A. Setio, Colin Jacobs
Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model. Medical Imaging: Computer-Aided Diagnosis 2015: 94141O - [c3]Francesco Ciompi
Automatic detection of spiculation of pulmonary nodules in computed tomography images. Medical Imaging: Computer-Aided Diagnosis 2015: 941409 - 2014
- [j1]Jens Petersen
Optimal surface segmentation using flow lines to quantify airway abnormalities in chronic obstructive pulmonary disease. Medical Image Anal. 18(3): 531-541 (2014) - 2013
- [c2]Aasa Feragen
Tree-Space Statistics and Approximations for Large-Scale Analysis of Anatomical Trees. IPMI 2013: 74-85 - 2012
- [c1]Aasa Feragen
A Hierarchical Scheme for Geodesic Anatomical Labeling of Airway Trees. MICCAI (3) 2012: 147-155
Coauthor Index
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