AIMC Topic: Image Processing, Computer-Assisted

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MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network.

BMC medical informatics and decision making
BACKGROUND: The digital pathology images obtain the essential information about the patient's disease, and the automated nuclei segmentation results can help doctors make better decisions about diagnosing the disease. With the speedy advancement of c...

Two-Stage CNN Model for Joint Demosaicing and Denoising of Burst Bayer Images.

Computational intelligence and neuroscience
In the classical image processing pipeline, demosaicing and denoising are separated steps that may interfere with each other. Joint demosaicing and denoising utilizes the shared image prior information to guide the image recovery process. It is expec...

A new dynamic deep learning noise elimination method for chip-based real-time PCR.

Analytical and bioanalytical chemistry
Point-of-care (POC) real-time polymerase chain reaction (PCR) has become one of the most important technologies for many fields such as pathogen detection and water-quality monitoring. POC real-time PCR usually adopts chips with small-volume chambers...

State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere.

Sensors (Basel, Switzerland)
Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-...

The auto segmentation for cardiac structures using a dual-input deep learning network based on vision saliency and transformer.

Journal of applied clinical medical physics
PURPOSE: Accurate segmentation of cardiac structures on coronary CT angiography (CCTA) images is crucial for the morphological analysis, measurement, and functional evaluation. In this study, we achieve accurate automatic segmentation of cardiac stru...

Neural Shape Parsers for Constructive Solid Geometry.

IEEE transactions on pattern analysis and machine intelligence
Constructive solid geometry (CSG) is a geometric modeling technique that defines complex shapes by recursively applying boolean operations on primitives such as spheres and cylinders. We present CSGNet, a deep network architecture that takes as input...

Optimizing Latent Distributions for Non-Adversarial Generative Networks.

IEEE transactions on pattern analysis and machine intelligence
The generator in generative adversarial networks (GANs) is driven by a discriminator to produce high-quality images through an adversarial game. At the same time, the difficulty of reaching a stable generator has been increased. This paper focuses on...

DiCENet: Dimension-Wise Convolutions for Efficient Networks.

IEEE transactions on pattern analysis and machine intelligence
We introduce a novel and generic convolutional unit, DiCE unit, that is built using dimension-wise convolutions and dimension-wise fusion. The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input t...

Deep Prior Approach for Room Impulse Response Reconstruction.

Sensors (Basel, Switzerland)
In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural networ...