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Found 8 papers, 3 papers with code
D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification
The integration of deep learning technologies in medical imaging aims to enhance the efficiency and accuracy of cancer diagnosis, particularly for pancreatic and breast cancers, which present significant diagnostic challenges due to their high mortality rates and complex imaging characteristics.
Unsupervised Training of a Dynamic Context-Aware Deep Denoising Framework for Low-Dose Fluoroscopic Imaging
Noise reduction is crucial for maintaining image quality, especially given such challenges as motion artifacts and the limited availability of clean data in medical imaging.
Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising
We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss.
Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements
We perform a simulation study using real motion recorded with an optical tracking system.
Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee
In our proposed multi-stage algorithm, these signals are transformed to the global coordinate system of the CT scan and applied for motion compensation during reconstruction.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis.
Precision Learning: Towards Use of Known Operators in Neural Networks
We demonstrate that use of known transforms is able to change maximal error bounds.
