Search Results for author:
Found 29 papers, 10 papers with code
Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising
Note that we use LDCT images based on the noisy-as-clean strategy for corruption instead of NDCT images.
NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing
NL-CS Net is composed of the up-sampling module and the recovery module.
Quadratic Graph Attention Network (Q-GAT) for Robust Construction of Gene Regulatory Networks
The former informs that nonlinear aggregation of quadratic neurons can amplify useful signals and suppress unwanted noise, thereby facilitating robustness, while the latter reveals that Q-GAT can leverage more features in prediction thanks to the dual attention mechanism, which endows Q-GAT with the ability to confront adversarial perturbation.
3D PETCT Tumor Lesion Segmentation via GCN Refinement
Secondly, the nnUNet result of the certainty nodes is used as label to form a semi-supervised graph network problem, and the uncertainty part is optimized through training the GCN network to improve the segmentation performance.
QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data
Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk.
Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
Noninvasive X-ray imaging of nanoscale three-dimensional objects, e. g. integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of complex electromagnetic field through ICs; and tomographic scanning, which collects complex field projections from multiple angles.
Deep learning at the edge enables real-time streaming ptychographic imaging
Coherent microscopy techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells.
Quasi-supervised Learning for Super-resolution PET
In this paper, we propose a quasi-supervised learning method, which is a new type of weakly-supervised learning methods, to recover HR PET images from LR counterparts by leveraging similarity between unpaired LR and HR image patches.
Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation
Nonnegative matrix factorization (NMF) has been widely used to dimensionality reduction in machine learning.
CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron
Cervical cytopathology image classification is an important method to diagnose cervical cancer.
Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation
With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently.
TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos
The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments.
Subspace Nonnegative Matrix Factorization for Feature Representation
Traditional nonnegative matrix factorization (NMF) learns a new feature representation on the whole data space, which means treating all features equally.
An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images
This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy.
Improving the Level of Autism Discrimination through GraphRNN Link Prediction
This paper is based on the latter technique, which learns the edge distribution of real brain network through GraphRNN, and generates the synthetic data which has incentive effect on the discriminant model.
A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements
This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation
Traditional machine learning methods achieve maximum accuracy of 76. 02% and deep learning method achieves a maximum accuracy of 95. 37%.
Real-time X-ray Phase-contrast Imaging Using SPINNet -- A Speckle-based Phase-contrast Imaging Neural Network
In addition to significant improvement in speed, our experimental results show that the imaging resolution and phase retrieval quality of SPINNet outperform existing single-shot speckle-based methods.
An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation
Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data.
AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imaging
The problem of phase retrieval, or the algorithmic recovery of lost phase information from measured intensity alone, underlies various imaging methods from astronomy to nanoscale imaging.
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer
In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation.
Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers
The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images.
A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches
In this review, first, we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods.
A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches
In this article, we have studied the development of microorganism counting methods using digital image analysis.
DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques
Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors.
A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification
The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module.
A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches
This paper reviews the methods of WSI analysis based on machine learning.
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks
EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions.
Review of Machine-Learning Methods for RNA Secondary Structure Prediction
Recently, with the increasing availability of RNA structure data, new methods based on machine-learning technologies, especially deep learning, have alleviated the issue.
