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Found 15 papers, 5 papers with code
FHDe²Net: Full High Definition Demoireing Network
Frequency aliasing in the digital capture of display screens leads to the moir´e pattern, appearing as stripe-shaped distortions in images.
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
In this work, we present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body.
RetinexFlow for CT metal artifact reduction
Metal artifacts is a major challenge in computed tomography (CT) imaging, significantly degrading image quality and making accurate diagnosis difficult.
Cross-Modal Causal Intervention for Medical Report Generation
Medical report generation (MRG) is essential for computer-aided diagnosis and medication guidance, which can relieve the heavy burden of radiologists by automatically generating the corresponding medical reports according to the given radiology image.
Active CT Reconstruction with a Learned Sampling Policy
Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision.
Universal Segmentation of 33 Anatomies
Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset.
3D endoscopic depth estimation using 3D surface-aware constraints
We propose a loss function for depth estimation that integrates the surface-aware constraints, leading to a faster and better convergence with the valid information from spatial information.
DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction
While Computed Tomography (CT) reconstruction from X-ray sinograms is necessary for clinical diagnosis, iodine radiation in the imaging process induces irreversible injury, thereby driving researchers to study sparse-view CT reconstruction, that is, recovering a high-quality CT image from a sparse set of sinogram views.
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Spine-related diseases have high morbidity and cause a huge burden of social cost.
Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation
More importantly, we show that using such a sinogram extrapolation module significantly improves the generalization capability of the model on unseen datasets (e. g., COVID-19 and LIDC datasets) when compared to existing approaches.
Recursive Training for Zero-Shot Semantic Segmentation
We propose a recursive training scheme to supervise the retraining of a semantic segmentation model for a zero-shot setting using a pseudo-feature representation.
First image then video: A two-stage network for spatiotemporal video denoising
This two-stage network, when trained in an end-to-end fashion, yields the state-of-the-art performances on the video denoising benchmark Vimeo90K dataset in terms of both denoising quality and computation.
Label-Removed Generative Adversarial Networks Incorporating with K-Means
Generative Adversarial Networks (GANs) have achieved great success in generating realistic images.
Emergent Quantum Mechanics in an Introspective Machine Learning Architecture
Can physical concepts and laws emerge in a neural network as it learns to predict the observation data of physical systems?
Disordered Systems and Neural Networks Quantum Physics
