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Found 7 papers, 1 papers with code
Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI
BACKGROUND AND PURPOSE: Deep learning has been demonstrated effective in many neuroimaging applications.
Spk2ImgNet: Learning To Reconstruct Dynamic Scene From Continuous Spike Stream
Different from the conventional digital cameras that compact the photoelectric information within the exposure interval into a single snapshot, the spike camera produces a continuous spike stream to record the dynamic light intensity variation process.
COLA-Net: Collaborative Attention Network for Image Restoration
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance.
TGA: Two-level Group Attention for Assembly State Detection
Assembly state detection, i. e., object state detection, has a critical meaning in computer vision tasks, especially in AR assisted assembly.
DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI
We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia.
Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation
In this way, the network is guaranteed to be aware of the class-dependent feature maps to facilitate the segmentation.
Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples
To estimate the expectation and variance parameters for the transform bands of a particular patch, we exploit the non-local correlation of image and collect a set of similar patches as data samples to form the distribution.
