no code implementations • 1 Mar 2024 • Athanasios Tragakis, Qianying Liu, Chaitanya Kaul, Swalpa Kumar Roy, Hang Dai, Fani Deligianni, Roderick Murray-Smith, Daniele Faccio
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance.
1 code implementation • 23 Aug 2023 • James K Ruffle, Robert J Gray, Samia Mohinta, Guilherme Pombo, Chaitanya Kaul, Harpreet Hyare, Geraint Rees, Parashkev Nachev
It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal.
no code implementations • 28 Feb 2023 • Kevin Mitchell, Khaled Kassem, Chaitanya Kaul, Valentin Kapitany, Philip Binner, Andrew Ramsay, Roderick Murray-Smith, Daniele Faccio
For widespread adoption, public security and surveillance systems must be accurate, portable, compact, and real-time, without impeding the privacy of the individuals being observed.
1 code implementation • 14 Oct 2022 • Qianying Liu, Chaitanya Kaul, Jun Wang, Christos Anagnostopoulos, Roderick Murray-Smith, Fani Deligianni
For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations.
2 code implementations • 1 Jun 2022 • Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith, Dirk Husmeier
To address this shortcoming, we propose The Fully Convolutional Transformer (FCT), which builds on the proven ability of Convolutional Neural Networks to learn effective image representations, and combines them with the ability of Transformers to effectively capture long-term dependencies in its inputs.
Ranked #1 on Medical Image Segmentation on ACDC
no code implementations • 25 Nov 2021 • Joshua Mitton, Chaitanya Kaul, Roderick Murray-Smith
Our rotation equivariant model outperforms state-of-the-art methods on a real-world dataset and we demonstrate that it accurately captures the shape and pose in the generated meshes under rotation of the input hand.
no code implementations • 21 Nov 2021 • Chaitanya Kaul, Joshua Mitton, Hang Dai, Roderick Murray-Smith
It achieves this feat due to its effectiveness in creating a novel and robust attention-based point set embedding through a convolutional projection layer crafted for processing dynamically local point set neighbourhoods.
no code implementations • 4 Jul 2021 • Marija Jegorova, Chaitanya Kaul, Charlie Mayor, Alison Q. O'Neil, Alexander Weir, Roderick Murray-Smith, Sotirios A. Tsaftaris
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data.
no code implementations • 7 Apr 2021 • Chaitanya Kaul, Nick Pears, Suresh Manandhar
The application of deep learning to 3D point clouds is challenging due to its lack of order.
no code implementations • 4 Dec 2019 • Chaitanya Kaul, Nick Pears, Hang Dai, Roderick Murray-Smith, Suresh Manandhar
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation.
no code implementations • 22 Oct 2019 • Chaitanya Kaul, Nick Pears, Hang Dai, Roderick Murray-Smith, Suresh Manandhar
Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth.
no code implementations • 18 May 2019 • Chaitanya Kaul, Nick Pears, Suresh Manandhar
But their application to processing data lying on non-Euclidean domains is still a very active area of research.
1 code implementation • 8 Feb 2019 • Chaitanya Kaul, Suresh Manandhar, Nick Pears
We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder.