334 citations · 1.3k across the 54 of their papers we have counts for
8 papers · 1 filter
Synthetic data for unsupervised polyp segmentation
Enric Moreu, Kevin McGuinness, Noel E. O'Connor
Deep learning has shown excellent performance in analysing medical images. However, datasets are difficult to obtain due privacy issues, standardization problems, and lack of annot…
Attention-based Stylisation for Exemplar Image Colourisation
Marc Gorriz Blanch, Issa Khalifeh, Alan Smeaton +2
Exemplar-based colourisation aims to add plausible colours to a grayscale image using the guidance of a colour reference image. Most of the existing methods tackle the task as a st…
Attention-Based Neural Networks for Chroma Intra Prediction in Video Coding
Marc Górriz, Saverio Blasi, Alan F. Smeaton +2
Neural networks can be successfully used to improve several modules of advanced video coding schemes. In particular, compression of colour components was shown to greatly benefit f…
Chroma Intra Prediction with attention-based CNN architectures
Marc Górriz, Saverio Blasi, Alan F. Smeaton +2
Neural networks can be used in video coding to improve chroma intra-prediction. In particular, usage of fully-connected networks has enabled better cross-component prediction with…
Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding
Luka Murn, Saverio Blasi, Alan F. Smeaton +2
Deep learning has shown great potential in image and video compression tasks. However, it brings bit savings at the cost of significant increases in coding complexity, which limits…
End-to-End Conditional GAN-based Architectures for Image Colourisation
Marc Górriz, Marta Mrak, Alan F. Smeaton +1
In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly ma…
Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images
Jaynal Abedin, Joseph Antony, Kevin McGuinness +4
Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments accordin…
Assessing Knee OA Severity with CNN attention-based end-to-end architectures
Marc Górriz, Joseph Antony, Kevin McGuinness +2
This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which i…