1.1k citations · 2.7k across the 92 of their papers we have counts for
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Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT
Saskia Glaser, Gabriel Maicas, Sergei Bedrikovetski +2
The diagnosis of the presence of metastatic lymph nodes from abdominal computed tomography (CT) scans is an essential task performed by radiologists to guide radiation and chemothe…
Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames
Yuyuan Liu, Yu Tian, Gabriel Maicas +4
The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such de…
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro +1
Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For e…
Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space
Rafael Felix, Ben Harwood, Michele Sasdelli +1
Generalised zero-shot learning (GZSL) is a classification problem where the learning stage relies on a set of seen visual classes and the inference stage aims to identify both the…
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces
Rafael Felix, Ben Harwood, Michele Sasdelli +1
Generalised zero-shot learning (GZSL) methods aim to classify previously seen and unseen visual classes by leveraging the semantic information of those classes. In the context of G…
Few-Shot Meta-Denoising
Leslie Casas, Attila Klimmek, Gustavo Carneiro +2
We study the problem of few-shot learning-based denoising where the training set contains just a handful of clean and noisy samples. A solution to mitigate the small training set i…
Unsupervised Task Design to Meta-Train Medical Image Classifiers
Gabriel Maicas, Cuong Nguyen, Farbod Motlagh +2
Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with smal…
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning. The proposed algor…
Bayesian Generative Active Deep Learning
Toan Tran, Thanh-Toan Do, Ian Reid +1
Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources…
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning
Thanh-Toan Do, Toan Tran, Ian Reid +3
We propose a method that substantially improves the efficiency of deep distance metric learning based on the optimization of the triplet loss function. One epoch of such training p…
Multi-modal Ensemble Classification for Generalized Zero Shot Learning
Rafael Felix, Michele Sasdelli, Ian Reid +1
Generalized zero shot learning (GZSL) is defined by a training process containing a set of visual samples from seen classes and a set of semantic samples from seen and unseen class…