A siamese network with adaptive gated feature fusion for individual knee OA features grades prediction

  title={A siamese network with adaptive gated feature fusion for individual knee OA features grades prediction},
  author={Kang Wang and Xin Niu and Yong Dou and Dongxing Xie and Tuo Yang},
  journal={Scientific Reports},
Grading individual knee osteoarthritis (OA) features is a fine-grained knee OA severity assessment. Existing methods ignore following problems: (1) more accurately located knee joints benefit subsequent grades prediction; (2) they do not consider knee joints’ symmetry and semantic information, which help to improve grades prediction performance. To this end, we propose a SE-ResNext50-32x4d-based Siamese network with adaptive gated feature fusion method to simultaneously assess eight tasks. In… 

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

  • X. Bokhimi
  • Chemistry
    Frontiers in Chemical Engineering
  • 2021
We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or



Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs Using Deep Convolutional Neural Networks

An automatic method to predict KL and OARSI grades from knee radiographs is developed based on Deep Learning and leverages an ensemble of residual networks with 50 layers, which is better than the current state-of-the-art.

Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity Using Convolutional Neural Networks

A new approach to automatically detect the knee joints using a fully convolutional neural network (FCN) to automatically quantify the severity of knee OA using X-ray images, with extremely promising results that outperform existing approaches.

Automatic quantification of radiographic knee osteoarthritis severity and associated diagnostic features using deep convolutional neural networks

This thesis investigates the use of machine learning algorithms and deep learning architectures, in particular convolutional neural networks (CNN), to quantify the severity and clinical radiographic features of knee OA.

Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks

It is argued that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy, which leads to the formulation of the prediction of KL grades as a regression problem and improves accuracy.

Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach

A new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale is presented.

Automatic Grading of Knee Osteoarthritis from Plain Radiographs Using Densely Connected Convolutional Networks

This paper considers densely connected convolutional networks and their applicability to the problem of assessment of knee osteoarthritis severity in the five-point Kellgren-Lawrence scale, and applies DenseNets to quantify OA stages in the images of detected knee joints.

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

A simple self-assessment scoring system and an improved artificial neural network model for knee OA may be useful for identifying the adults at high risk for kneeOA and an ANN calculator to simply predict the knee Oa risk is provided.

A Novel Method for Automatic Localization of Joint Area on Knee Plain Radiographs

A novel and computationally efficient method to automatically annotate conventional knee radiographs within 14–16 ms and high resolution ones within 170 ms is developed and demonstrated that the developed method is suitable for large-scale analyses.