Radiologist with a strong interest in Magnetic Resonance, Informatics and Artificial Intelligence.
Heidelberg, Germany
Joined August 2020
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Just published: Adrian Schrader evaluates risk modeling of Prostata cancer using AI, nomograms and PI-RADS. link.springer.com/article/10…
In this work by Netzer et al. we further investigate the generalizability of prostate MRI deep learning for prostate cancer detection, while utilizing the previously proposed dynamic threshold calibration approach link.springer.com/article/10…
In this work by Cedric Weißer et al. we examine weakly supervised MRI Slice‐Level Deep Learning Classification of Prostate Cancer and find it approximates Full Voxel‐ and Slice‐Level Annotation with increasing training set size onlinelibrary.wiley.com/doi/…
pubmed.ncbi.nlm.nih.gov/3733…
We systematically assessed repeatability of ADC in PI-RADS lesions before and after repositioning of the patient in the MRI scanner.
David Bonekamp MD retweeted
In this retrospective analysis of multiparametric prostate MRI, quantitative or qualitative dynamic contrast-enhanced MRI did not assist with PI-RADS 3 lesion risk stratification. @DKFZ @uniklinik_hd bit.ly/3Psgei4
Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or… pubmed.ncbi.nlm.nih.gov/3414…
See our latest work on
Fully Automatic Deep Learning in Bi-institutional Prostate Magnetic Resonance Imaging: Effects of Cohort Size and H… pubmed.ncbi.nlm.nih.gov/3404…
Unser frischer Reviewartikel zu ML in mpMRT der Prostata #DeepLearning #MachineLearning #review #urology #prostatecancer #mri springermedizin.de/maschinel…
Comparison of single-scanner single-protocol quantitative ADC measurements to ADC ratios to detect clinically signi… pubmed.ncbi.nlm.nih.gov/3348…
When using a single MR system and MR protocol, we can estimate the variation of time point- related and biolological factors and find that none of the calibrations to reference tissues is better than quantitative ADC. Let's improve methods to calibrate scanners and protocols...
David Bonekamp MD retweeted
ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate sciencedirect.com/science/ar…
"Very important step to understand power of any imaging: histology-imaging registration. A #deeplearning based approach is presented in this paper"
Our Radiology Publication entitled "Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment" is in the top 5% of Altmetric output, thank you all very much for your interest in our work and your support pubs.rsna.org/doi/10.1148/ra…
Continued sliding-window re-calibration of a DL system leads to more stable performance. Have you tried this with your deep learning systems ?pubmed.ncbi.nlm.nih.gov/3276…
Mean Dice coefficient of 4 independent manual prostate MRI lesion assessments was only moderate 0.48-0.52, providing a benchmark for DL systems. A fully automatic DL system achieved Dice of 0.22, a secondary quality criterion? pubmed.ncbi.nlm.nih.gov/3321…
Very nice work, apparently initially not accepted at MICCAI, truly surprising performance. Setting the heuristics and parameters in such effective way reflects the impact of your multi-domain expertise.
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Pubmed link of our newest article, currently pdf link not shown, see previous tweet for pdf link: Comparison of Prostate MRI Lesion Segmentation Agreement Between Multiple Radiologists and a Fully Automatic Deep L… pubmed.ncbi.nlm.nih.gov/3321…
Direct comparison of prostate MRI lesion segmentations performed by a deep learning system and multiple radiologists, allowing adjustment of Dice score for human to machine comparisons thieme-connect.com/products/…
We find no advantage of b-values higher than 1000-1500 or advanced modeling for assessment of sPC by MRI. See our new publication pubmed.ncbi.nlm.nih.gov/3293…
