Search Results for author:
Found 16 papers, 9 papers with code
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e. g., high broad-spectrum potency and low toxicity.
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery.
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data.
Ranked #5 on
Anomaly Detection
on AeBAD-V
Ultrasound Video Summarization using Deep Reinforcement Learning
We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
Detecting Outliers with Foreign Patch Interpolation
A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor.
nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
It is also difficult to assess whether a task generalises well for universal anomaly detection, as they are often only tested on a limited range of anomalies.
Detecting Outliers with Poisson Image Interpolation
We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection.
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in their training.
Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging
We have trained this network using ~4000 labeled trans-rectal ultrasound images and tested on an independent set of images with ground truth landmark locations.
Semi-supervised Learning of Fetal Anatomy from Ultrasound
Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images.
Divergent Search for Few-Shot Image Classification
We search for regions in the behavior space that the current archive cannot reach.
Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening
In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound.
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome
Congenital heart disease is considered as one the most common groups of congenital malformations which affects $6-11$ per $1000$ newborns.
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts.
Adnexal Mass Segmentation with Ultrasound Data Synthesis
Ovarian cancer is the most lethal gynaecological malignancy.
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis.
Out of Distribution (OOD) Detection
Self-Supervised Anomaly Detection
+1
