J. Hofmanninger

发表

Maschinelles Lernen in der Radiologie

S. Röhrich, G. Langs, U. Attenberger, 2020, Der Radiologe.

Machine learning: from radiomics to discovery and routine

S. Röhrich, G. Langs, J. Hofmanninger, 2018, Der Radiologe.

[Artificial intelligence in lung imaging].

S. Röhrich, J. Hofmanninger, H. Prosch, 2019, Der Radiologe.

Volumetry based biomarker speed of growth: Quantifying the change of total tumor volume in whole-body magnetic resonance imaging over time improves risk stratification of smoldering multiple myeloma patients

Georg Langs, Bjoern H. Menze, Bjoern H Menze, 2018, Oncotarget.

Variability of computed tomography radiomics features of fibrosing interstitial lung disease: A test-retest study.

S. Röhrich, G. Langs, Michael Weber, 2020, Methods.

Unsupervised machine learning identifies predictive progression markers of IPF

S. Röhrich, G. Langs, V. Poletti, 2022, European Radiology.

Early Predictors of Bone Infiltration in Multiple Myeloma Patients from T 2 weighted MRI Images

Bjoern H Menze, G. Langs, M. Weber, 2018 .

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Angelica I. Avilés-Rivero, J. H. Rudd, Mishal N. Patel, 2020, Nature Machine Intelligence.

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

S. Röhrich, G. Langs, J. Hofmanninger, 2020, European Radiology Experimental.

Heterogeneity and matching of ventilation and perfusion within anatomical lung units in rats

M. Krueger, R. Glenny, J. Hofmanninger, 2013, Respiratory Physiology & Neurobiology.

Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma.

Georg Langs, Bjoern H Menze, Bjoern Menze, 2019, 1907.13539.

Predicting Future Bone Infiltration Patterns in Multiple Myeloma

Georg Langs, Marc-André Weber, Björn H. Menze, 2018, Patch-MI@MICCAI.

Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT.

S. Röhrich, G. Langs, J. Hofmanninger, 2020, Radiology. Cardiothoracic imaging.

Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging

Oleg S. Pianykh, O. Pianykh, G. Langs, 2021, Nature Communications.

EP13.01-011 Combining Automated Malignancy Risk Estimation with Lung Nodule Detection May Reduce Physician Effort and Increase Diagnostic Accuracy

J. Hofmanninger, G. Langs, M. Calhoun, 2022, Journal of Thoracic Oncology.

Dynamic memory to alleviate catastrophic forgetting in continuous learning settings pdf

Georg Langs, Johannes Hofmanninger, Oleg Pianykh, 2020, MICCAI.

Mapping visual features to semantic profiles for retrieval in medical imaging

Georg Langs, Johannes Hofmanninger, G. Langs, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Effects of individualized electrical impedance tomography and image reconstruction settings upon the assessment of regional ventilation distribution: Comparison to 4-dimensional computed tomography in a porcine model

Eugenijus Kaniusas, Stefan Kampusch, Florian Thürk, 2017, PloS one.

Unsupervised machine learning identifies predictive progression markers of IPF

S. Röhrich, G. Langs, V. Poletti, 2022, European Radiology.

Unsupervised Identification of Clinically Relevant Clusters in Routine Imaging Data

Georg Langs, Markus Holzer, Thomas Schlegl, 2016, MICCAI.

Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics

G. Langs, J. Hofmanninger, H. Prosch, 2021, Machine Learning for Biomedical Imaging.

Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition

Georg Langs, Johannes Hofmanninger, Matthias Perkonigg, 2021, IPMI.

Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after trauma

S. Röhrich, G. Langs, J. Hofmanninger, 2021, European Radiology.

Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma

G. Langs, C. Mitter, J. Hofmanninger, 2023, Neuro-oncology advances.