Marcus R. Makowski
Orcid: 0000-0001-8778-647X
According to our database1,
Marcus R. Makowski
authored at least 31 papers
between 2020 and 2024.
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Bibliography
2024
Expert Syst. Appl., March, 2024
npj Digit. Medicine, 2024
Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data.
CoRR, 2024
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection.
CoRR, 2024
Unifying Local and Global Shape Descriptors to Grade Soft-Tissue Sarcomas Using Graph Convolutional Networks.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
2023
Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.
Comput. Methods Programs Biomed., June, 2023
From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties.
CoRR, 2023
Evaluation of GPT-4 for chest X-ray impression generation: A reader study on performance and perception.
CoRR, 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology.
CoRR, 2023
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT Scans.
IEEE Access, 2023
2022
Hierarchical Multi-Resolution Graph-Cuts for Water-Fat-Silicone Separation in Breast MRI.
IEEE Trans. Medical Imaging, 2022
Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.
npj Digit. Medicine, 2022
Iodine Images in Dual-energy CT: Detection of Hepatic Steatosis by Quantitative Iodine Concentration Values.
J. Digit. Imaging, 2022
Prostate158 - An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection.
Comput. Biol. Medicine, 2022
Longitudinal Analysis of Disease Progression Using Image and Laboratory Data for Covid-19 Patients.
Proceedings of the Bildverarbeitung für die Medizin 2022, 2022
2021
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.
npj Digit. Medicine, 2021
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning.
Nat. Mach. Intell., 2021
Nat. Mach. Intell., 2021
Per-Pixel Lung Thickness and Lung Capacity Estimation on Chest X-Rays using Convolutional Neural Networks.
CoRR, 2021
Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry.
CoRR, 2021
CoRR, 2021
Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty.
CoRR, 2021
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation.
CoRR, 2021
Differentially private federated deep learning for multi-site medical image segmentation.
CoRR, 2021
3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware.
CoRR, 2021
Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports.
Bioinform., 2021
2020
Nat. Mach. Intell., 2020
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction.
CoRR, 2020