1.1k citations · 2.7k across the 92 of their papers we have counts for
24 papers · 1 filter
A Hierarchical Multi-Task Approach to Gastrointestinal Image Analysis
Adrian Galdran, Gustavo Carneiro, Miguel A. González Ballester
A large number of different lesions and pathologies can affect the human digestive system, resulting in life-threatening situations. Early detection plays a relevant role in the su…
Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification
Adrian Galdran, Gustavo Carneiro, Miguel A. González Ballester
This paper compares well-established Convolutional Neural Networks (CNNs) to recently introduced Vision Transformers for the task of Diabetic Foot Ulcer Classification, in the cont…
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
Fengbei Liu, Yu Tian, Yuanhong Chen +3
Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and mult…
Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
Yuyuan Liu, Yu Tian, Yuanhong Chen +3
Consistency learning using input image, feature, or network perturbations has shown remarkable results in semi-supervised semantic segmentation, but this approach can be seriously…
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
Yu Tian, Yuyuan Liu, Guansong Pang +3
State-of-the-art (SOTA) anomaly segmentation approaches on complex urban driving scenes explore pixel-wise classification uncertainty learned from outlier exposure, or external rec…
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels
Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid +1
The most competitive noisy label learning methods rely on an unsupervised classification of clean and noisy samples, where samples classified as noisy are re-labelled and "MixMatch…
Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation
Adrian Galdran, Gustavo Carneiro, Miguel A. González Ballester
Polyps represent an early sign of the development of Colorectal Cancer. The standard procedure for their detection consists of colonoscopic examination of the gastrointestinal trac…
Balanced-MixUp for Highly Imbalanced Medical Image Classification
Adrian Galdran, Gustavo Carneiro, Miguel A. González Ballester
Highly imbalanced datasets are ubiquitous in medical image classification problems. In such problems, it is often the case that rare classes associated to less prevalent diseases a…
Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images
Yu Tian, Fengbei Liu, Guansong Pang +5
Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images…
3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training
Yaqub Jonmohamadi, Shahnewaz Ali, Fengbei Liu +4
Minimally invasive surgery (MIS) has many documented advantages, but the surgeon's limited visual contact with the scene can be problematic. Hence, systems that can help surgeons n…
Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging
Minh-Son To, Ian G Sarno, Chee Chong +2
Longitudinal imaging forms an essential component in the management and follow-up of many medical conditions. The presence of lesion changes on serial imaging can have significant…
Probabilistic task modelling for meta-learning
Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro
We propose probabilistic task modelling -- a generative probabilistic model for collections of tasks used in meta-learning. The proposed model combines variational auto-encoding an…
Kernel Adversarial Learning for Real-world Image Super-resolution
Hu Wang, Congbo Ma, Jianpeng Zhang +2
Current deep image super-resolution (SR) approaches aim to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and addit…
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
Ragav Sachdeva, Filipe R Cordeiro, Vasileios Belagiannis +2
We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe label noise and competitiv…
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Filipe R. Cordeiro, Ragav Sachdeva, Vasileios Belagiannis +2
Deep neural network models are robust to a limited amount of label noise, but their ability to memorise noisy labels in high noise rate problems is still an open issue. The most co…
NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification
Fengbei Liu, Yuanhong Chen, Yu Tian +4
Real-world large-scale medical image analysis (MIA) datasets have three challenges: 1) they contain noisy-labelled samples that affect training convergence and generalisation, 2) t…
Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
Fengbei Liu, Yu Tian, Filipe R. Cordeiro +3
The training of deep learning models generally requires a large amount of annotated data for effective convergence and generalisation. However, obtaining high-quality annotations i…
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
Yu Tian, Guansong Pang, Fengbei Liu +5
Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not confor…
Post-hoc Overall Survival Time Prediction from Brain MRI
Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento +1
Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOT…
Similarity of Classification Tasks
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
Recent advances in meta-learning has led to remarkable performances on several few-shot learning benchmarks. However, such success often ignores the similarity between training and…
Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning
Yu Tian, Leonardo Zorron Cheng Tao Pu, Yuyuan Liu +6
In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulate…
Semantics for Robotic Mapping, Perception and Interaction: A Survey
Sourav Garg, Niko Sünderhauf, Feras Dayoub +9
For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and rela…
Deep One-Class Classification via Interpolated Gaussian Descriptor
Yuanhong Chen, Yu Tian, Guansong Pang +1
One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data descript…
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
Yu Tian, Guansong Pang, Yuanhong Chen +3
Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing a…