Müller-Franzes G - Search Results

1

Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks.

Müller-Franzes G, Nolte T, Ciba M, Schock J, Khader F, Prescher A, Wilms LM, Kuhl C, Nebelung S, Truhn D. Müller-Franzes G, et al. Diagnostics (Basel). 2022 Mar 11;12(3):688. doi: 10.3390/diagnostics12030688. Diagnostics (Basel). 2022. PMID: 35328240 Free PMC article.

2

Radiomics feature reproducibility under inter-rater variability in segmentations of CT images.

Haarburger C, Müller-Franzes G, Weninger L, Kuhl C, Truhn D, Merhof D. Haarburger C, et al. Sci Rep. 2020 Jul 29;10(1):12688. doi: 10.1038/s41598-020-69534-6. Sci Rep. 2020. PMID: 32728098 Free PMC article.

3

A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis.

Tietz E, Truhn D, Müller-Franzes G, Berres ML, Hamesch K, Lang SA, Kuhl CK, Bruners P, Schulze-Hagen M. Tietz E, et al. Diagnostics (Basel). 2021 Sep 9;11(9):1650. doi: 10.3390/diagnostics11091650. Diagnostics (Basel). 2021. PMID: 34573991 Free PMC article.

4

Author Correction: Radiomics feature reproducibility under inter-rater variability in segmentations of CT images.

Haarburger C, Müller-Franzes G, Weninger L, Kuhl C, Truhn D, Merhof D. Haarburger C, et al. Sci Rep. 2021 Nov 16;11(1):22670. doi: 10.1038/s41598-021-02114-4. Sci Rep. 2021. PMID: 34785700 Free PMC article. No abstract available.

5

Reliability as a Precondition for Trust-Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction.

Müller-Franzes G, Nebelung S, Schock J, Haarburger C, Khader F, Pedersoli F, Schulze-Hagen M, Kuhl C, Truhn D. Müller-Franzes G, et al. Diagnostics (Basel). 2022 Jan 19;12(2):247. doi: 10.3390/diagnostics12020247. Diagnostics (Basel). 2022. PMID: 35204338 Free PMC article.

6

Novel machine learning algorithm can identify patients at risk of poor overall survival following curative resection for colorectal liver metastases.

Amygdalos I, Müller-Franzes G, Bednarsch J, Czigany Z, Ulmer TF, Bruners P, Kuhl C, Neumann UP, Truhn D, Lang SA. Amygdalos I, et al. J Hepatobiliary Pancreat Sci. 2023 May;30(5):602-614. doi: 10.1002/jhbp.1249. Epub 2022 Oct 25. J Hepatobiliary Pancreat Sci. 2023. PMID: 36196525

7

Artificial Intelligence for Clinical Interpretation of Bedside Chest Radiographs.

Khader F, Han T, Müller-Franzes G, Huck L, Schad P, Keil S, Barzakova E, Schulze-Hagen M, Pedersoli F, Schulz V, Zimmermann M, Nebelung L, Kather J, Hamesch K, Haarburger C, Marx G, Stegmaier J, Kuhl C, Bruners P, Nebelung S, Truhn D. Khader F, et al. Radiology. 2023 Apr;307(1):e220510. doi: 10.1148/radiol.220510. Epub 2022 Dec 6. Radiology. 2023. PMID: 36472534

8

Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images.

Müller-Franzes G, Huck L, Tayebi Arasteh S, Khader F, Han T, Schulz V, Dethlefsen E, Kather JN, Nebelung S, Nolte T, Kuhl C, Truhn D. Müller-Franzes G, et al. Radiology. 2023 May;307(3):e222211. doi: 10.1148/radiol.222211. Epub 2023 Mar 21. Radiology. 2023. PMID: 36943080

9

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

Busch F, Xu L, Sushko D, Weidlich M, Truhn D, Müller-Franzes G, Heimer MM, Niehues SM, Makowski MR, Hinsche M, Vahldiek JL, Aerts HJ, Adams LC, Bressem KK. Busch F, et al. Comput Methods Programs Biomed. 2023 Jun;234:107505. doi: 10.1016/j.cmpb.2023.107505. Epub 2023 Mar 22. Comput Methods Programs Biomed. 2023. PMID: 37003043

10

Denoising diffusion probabilistic models for 3D medical image generation.

Khader F, Müller-Franzes G, Tayebi Arasteh S, Han T, Haarburger C, Schulze-Hagen M, Schad P, Engelhardt S, Baeßler B, Foersch S, Stegmaier J, Kuhl C, Nebelung S, Kather JN, Truhn D. Khader F, et al. Sci Rep. 2023 May 5;13(1):7303. doi: 10.1038/s41598-023-34341-2. Sci Rep. 2023. PMID: 37147413 Free PMC article.

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