30 citations · 92 across the 8 of their papers we have counts for
6 papers · 1 filter
LinGuinE: Longitudinal Guidance Estimation for Volumetric Tumour Segmentation
Nadine Garibli, Mayank Patwari, Bence Csiba +2
Longitudinal volumetric tumour segmentation is critical for radiotherapy planning and response assessment, yet this problem is underexplored and most methods produce single-timepoi…
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Fabian Wagner, Mareike Thies, Felix Denzinger +7
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning~(…
Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning
Mayank Patwari, Ralf Gutjahr, Rainer Raupach +1
The use of deep learning has successfully solved several problems in the field of medical imaging. Deep learning has been applied to the CT denoising problem successfully. However,…
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography
Fabian Wagner, Mareike Thies, Mingxuan Gu +9
Computed tomography is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution and radiation dose…
Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization
Mayank Patwari, Ralf Gutjahr, Rainer Raupach +1
Denoising of clinical CT images is an active area for deep learning research. Current clinically approved methods use iterative reconstruction methods to reduce the noise in CT ima…
JBFnet -- Low Dose CT Denoising by Trainable Joint Bilateral Filtering
Mayank Patwari, Ralf Gutjahr, Rainer Raupach +1
Deep neural networks have shown great success in low dose CT denoising. However, most of these deep neural networks have several hundred thousand trainable parameters. This, combin…