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08/09/2023
SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation
Generalisation of deep neural networks becomes vulnerable when distribut...
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07/26/2023
Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling
The missing modality issue is critical but non-trivial to be solved by m...
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05/31/2023
Noisy-label Learning with Sample Selection based on Noise Rate Estimate
Noisy-labels are challenging for deep learning due to the high capacity ...
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03/20/2023
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
Noisy labels present a significant challenge in deep learning because mo...
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03/02/2023
Multi-Head Multi-Loss Model Calibration
Delivering meaningful uncertainty estimates is essential for a successfu...
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01/04/2023
Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Approach
Learning from noisy labels plays an important role in the deep learning ...
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01/04/2023
Task Weighting in Meta-learning with Trajectory Optimisation
Developing meta-learning algorithms that are un-biased toward a subset o...
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11/18/2022
Knowing What to Label for Few Shot Microscopy Image Cell Segmentation
In microscopy image cell segmentation, it is common to train a deep neur...
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10/17/2022
Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels
Many state-of-the-art noisy-label learning methods rely on learning mech...
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09/26/2022
Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models
State-of-the-art (SOTA) deep learning mammogram classifiers, trained wit...
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09/21/2022
Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation
When analysing screening mammograms, radiologists can naturally process ...
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09/13/2022
On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness
We study the impact of different loss functions on lesion segmentation f...
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09/02/2022
Instance-Dependent Noisy Label Learning via Graphical Modelling
Noisy labels are unavoidable yet troublesome in the ecosystem of deep le...
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08/23/2022
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels
Label noise is common in large real-world datasets, and its presence har...
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08/23/2022
An Evolutionary Approach for Creating of Diverse Classifier Ensembles
Classification is one of the most studied tasks in data mining and machi...
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08/17/2022
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
Meta-learning is an effective method to handle imbalanced and noisy-labe...
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08/03/2022
Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
Deep neural networks currently deliver promising results for microscopy ...
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07/22/2022
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction
Multi-modal learning focuses on training models by equally combining mul...
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06/20/2022
Test Time Transform Prediction for Open Set Histopathological Image Recognition
Tissue typology annotation in Whole Slide histological images is a compl...
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05/26/2022
Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images
Survival time prediction from medical images is important for treatment ...
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04/28/2022
Mixup-based Deep Metric Learning Approaches for Incomplete Supervision
Deep learning architectures have achieved promising results in different...
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03/28/2022
Translation Consistent Semi-supervised Segmentation for 3D Medical Images
3D medical image segmentation methods have been successful, but their de...
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03/03/2022
Semantic-guided Image Virtual Attribute Learning for Noisy Multi-label Chest X-ray Classification
Deep learning methods have shown outstanding classification accuracy in ...
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01/25/2022
Mutual information neural estimation for unsupervised multi-modal registration of brain images
Many applications in image-guided surgery and therapy require fast and r...
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11/25/2021
ACPL: Anti-curriculum Pseudo-labelling forSemi-supervised Medical Image Classification
Effective semi-supervised learning (SSL) in medical im-age analysis (MIA...
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11/25/2021
Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
Consistency learning using input image, feature, or network perturbation...
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11/12/2021
Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification
This paper compares well-established Convolutional Neural Networks (CNNs...
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10/22/2021
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels
The most competitive noisy label learning methods rely on an unsupervise...
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10/05/2021
Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation
Polyps represent an early sign of the development of Colorectal Cancer. ...
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09/20/2021
Balanced-MixUp for Highly Imbalanced Medical Image Classification
Highly imbalanced datasets are ubiquitous in medical image classificatio...
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06/10/2021
3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training
Minimally invasive surgery (MIS) has many documented advantages, but the...
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06/09/2021
Probabilistic task modelling for meta-learning
We propose probabilistic task modelling – a generative probabilistic mod...
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06/02/2021
Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging
Longitudinal imaging forms an essential component in the management and ...
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03/21/2021
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
In this paper, we address the problem of training deep neural networks i...
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03/06/2021
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Deep neural network models are robust to a limited amount of label noise...
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03/06/2021
Noisy Label Learning for Large-scale Medical Image Classification
The classification accuracy of deep learning models depends not only on ...
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03/05/2021
Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
The training of deep learning models generally requires a large amount o...
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03/05/2021
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
Unsupervised anomaly detection (UAD) learns one-class classifiers exclus...
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02/22/2021
Post-hoc Overall Survival Time Prediction from Brain MRI
Overall survival (OS) time prediction is one of the most common estimate...
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01/27/2021
Similarity of Classification Tasks
Recent advances in meta-learning has led to remarkable performances on s...
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01/25/2021
Unsupervised Anomaly Detection and Localisation with Multi-scale Interpolated Gaussian Descriptors
Current unsupervised anomaly detection and localisation systems are comm...
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01/25/2021
Weakly-supervised Video Anomaly Detection with Contrastive Learning of Long and Short-range Temporal Features
In this paper, we address the problem of weakly-supervised video anomaly...
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01/09/2021
Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning
In this paper, we propose and analyse a system that can automatically de...
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01/02/2021
Semantics for Robotic Mapping, Perception and Interaction: A Survey
For robots to navigate and interact more richly with the world around th...
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12/27/2020
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)
Domain generalisation (DG) methods address the problem of domain shift, ...
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12/05/2020
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?
Noisy Labels are commonly present in data sets automatically collected f...
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11/11/2020
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
The efficacy of deep learning depends on large-scale data sets that have...
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09/09/2020
Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding
Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample s...
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07/05/2020
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy
Intra-operative automatic semantic segmentation of knee joint structures...
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06/29/2020