37 citations · 90 across the 19 of their papers we have counts for
19 papers
AI-Enabled sensor fusion of time of flight imaging and mmwave for concealed metal detection
Chaitanya Kaul, Kevin J. Mitchell, Khaled Kassem +6
In the field of detection and ranging, multiple complementary sensing modalities may be used to enrich the information obtained from a dynamic scene. One application of this sensor…
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
Athanasios Tragakis, Qianying Liu, Chaitanya Kaul +5
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. We re…
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
Marco Aversa, Gabriel Nobis, Miriam Hägele +10
We present DiffInfinite, a hierarchical diffusion model that generates arbitrarily large histological images while preserving long-range correlation structural information. Our app…
mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor
Kevin Mitchell, Khaled Kassem, Chaitanya Kaul +5
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…
Continuous interaction with a smart speaker via low-dimensional embeddings of dynamic hand pose
Songpei Xu, Chaitanya Kaul, Xuri Ge +1
This paper presents a new continuous interaction strategy with visual feedback of hand pose and mid-air gesture recognition and control for a smart music speaker, which utilizes on…
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
Luis Oala, Marco Aversa, Gabriel Nobis +10
Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. Howeve…
Optimizing Vision Transformers for Medical Image Segmentation
Qianying Liu, Chaitanya Kaul, Jun Wang +3
For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture…
Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres
Joshua Mitton, Simon Peter Mekhail, Miles Padgett +3
We develop a new type of model for solving the task of inverting the transmission effects of multi-mode optical fibres through the construction of an -equivar…
The Fully Convolutional Transformer for Medical Image Segmentation
Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith +1
We propose a novel transformer model, capable of segmenting medical images of varying modalities. Challenges posed by the fine grained nature of medical image analysis mean that th…
Improving Sequential Query Recommendation with Immediate User Feedback
Shameem A Puthiya Parambath, Christos Anagnostopoulos, Roderick Murray-Smith
We propose an algorithm for next query recommendation in interactive data exploration settings, like knowledge discovery for information gathering. The state-of-the-art query recom…
Rotation Equivariant 3D Hand Mesh Generation from a Single RGB Image
Joshua Mitton, Chaitanya Kaul, Roderick Murray-Smith
We develop a rotation equivariant model for generating 3D hand meshes from 2D RGB images. This guarantees that as the input image of a hand is rotated the generated mesh undergoes…
CpT: Convolutional Point Transformer for 3D Point Cloud Processing
Chaitanya Kaul, Joshua Mitton, Hang Dai +1
We present CpT: Convolutional point Transformer - a novel deep learning architecture for dealing with the unstructured nature of 3D point cloud data. CpT is an improvement over exi…
Subgraph Permutation Equivariant Networks
Joshua Mitton, Roderick Murray-Smith
In this work we develop a new method, named Sub-graph Permutation Equivariant Networks (SPEN), which provides a framework for building graph neural networks that operate on sub-gra…
Rotation Equivariant Deforestation Segmentation and Driver Classification
Joshua Mitton, Roderick Murray-Smith
Deforestation has become a significant contributing factor to climate change and, due to this, both classifying the drivers and predicting segmentation maps of deforestation has at…
Adversarial learning of cancer tissue representations
Adalberto Claudio Quiros, Nicolas Coudray, Anna Yeaton +4
Deep learning based analysis of histopathology images shows promise in advancing the understanding of tumor progression, tumor micro-environment, and their underpinning biological…
Survey: Leakage and Privacy at Inference Time
Marija Jegorova, Chaitanya Kaul, Charlie Mayor +4
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…
A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs
Joshua Mitton, Hans M. Senn, Klaas Wynne +1
We propose a combination of a variational autoencoder and a transformer based model which fully utilises graph convolutional and graph pooling layers to operate directly on graphs.…
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Francesco Tonolini, Pablo G. Moreno, Andreas Damianou +1
We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean valu…
Variational Inference for Computational Imaging Inverse Problems
Francesco Tonolini, Jack Radford, Alex Turpin +2
Machine learning methods for computational imaging require uncertainty estimation to be reliable in real settings. While Bayesian models offer a computationally tractable way of re…