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09/19/2023
Analysing race and sex bias in brain age prediction
Brain age prediction from MRI has become a popular imaging biomarker ass...
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08/14/2023
Distance Matters For Improving Performance Estimation Under Covariate Shift
Performance estimation under covariate shift is a crucial component of s...
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08/14/2023
Robustness Stress Testing in Medical Image Classification
Deep neural networks have shown impressive performance for image-based d...
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07/31/2023
No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging
As machine learning methods gain prominence within clinical decision-mak...
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07/18/2023
Unsupervised Conditional Slot Attention for Object Centric Learning
Extracting object-level representations for downstream reasoning tasks i...
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07/11/2023
A Causal Ordering Prior for Unsupervised Representation Learning
Unsupervised representation learning with variational inference relies h...
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07/06/2023
The Role of Subgroup Separability in Group-Fair Medical Image Classification
We investigate performance disparities in deep classifiers. We find that...
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05/30/2023
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation
This paper presents an effective and general data augmentation framework...
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02/02/2023
Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation
Purpose: In curriculum learning, the idea is to train on easier samples ...
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01/01/2023
Image To Tree with Recursive Prompting
Extracting complex structures from grid-based data is a common key step ...
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12/22/2022
Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding
Curriculum learning and self-paced learning are the training strategies ...
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10/29/2022
A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging
Graph neural networks have emerged as a promising approach for the analy...
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10/17/2022
Visual Debates
The natural way of obtaining different perspectives on any given topic i...
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09/20/2022
Frequency Dropout: Feature-Level Regularization via Randomized Filtering
Deep convolutional neural networks have shown remarkable performance on ...
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09/07/2022
Risk of Bias in Chest X-ray Foundation Models
Foundation models are considered a breakthrough in all applications of A...
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08/23/2022
Deep Structural Causal Shape Models
Causal reasoning provides a language to ask important interventional and...
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07/05/2022
Vector Quantisation for Robust Segmentation
The reliability of segmentation models in the medical domain depends on ...
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07/05/2022
GLANCE: Global to Local Architecture-Neutral Concept-based Explanations
Most of the current explainability techniques focus on capturing the imp...
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07/05/2022
Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models
Explanations for black-box models help us understand model decisions as ...
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06/27/2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
Machine learning models deployed on medical imaging tasks must be equipp...
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05/27/2022
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed
Failure detection in automated image classification is a critical safegu...
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01/19/2022
Investigating underdiagnosis of AI algorithms in the presence of multiple sources of dataset bias
Deep learning models have shown great potential for image-based diagnosi...
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01/08/2022
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation
Domain Adaptation (DA) has recently raised strong interests in the medic...
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10/27/2021
Algorithmic encoding of protected characteristics and its implications on disparities across subgroups
It has been rightfully emphasized that the use of AI for clinical decisi...
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10/08/2021
Is MC Dropout Bayesian?
MC Dropout is a mainstream "free lunch" method in medical imaging for ap...
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09/30/2021
DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization
In recent years, the research landscape of machine learning in medical i...
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09/11/2021
Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition
Despite impressive accuracy, deep neural networks are often miscalibrate...
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08/11/2021
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Using publicly available data to determine the performance of methodolog...
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08/10/2021
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data
Convolutional Neural Networks (CNNs) are widely used for image classific...
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07/31/2021
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
Datasets are rarely a realistic approximation of the target population. ...
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07/20/2021
A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions
Despite technological and medical advances, the detection, interpretatio...
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07/19/2021
Transductive image segmentation: Self-training and effect of uncertainty estimation
Semi-supervised learning (SSL) uses unlabeled data during training to le...
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07/16/2021
Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification
Whole body magnetic resonance imaging (WB-MRI) is the recommended modali...
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07/13/2021
Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift
Scarcity of high quality annotated images remains a limiting factor for ...
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07/06/2021
Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis
Image classification models deployed in the real world may receive input...
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04/28/2021
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation
We propose a parameter efficient Bayesian layer for hierarchical convolu...
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04/12/2021
Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations
The task of image segmentation is inherently noisy due to ambiguities re...
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04/12/2021
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is increasing at an eno...
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02/20/2021
Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation
Class imbalance poses a challenge for developing unbiased, accurate pred...
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12/18/2020
Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks
Deep learning models for semantic segmentation are able to learn powerfu...
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10/28/2020
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
We investigate the usefulness of Wasserstein-2 kernels in the context of...
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09/29/2020
Cranial Implant Design via Virtual Craniectomy with Shape Priors
Cranial implant design is a challenging task, whose accuracy is crucial ...
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07/07/2020
Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy
Decompressive craniectomy (DC) is a common surgical procedure consisting...
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06/24/2020
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders
We introduce Post-DAE, a post-processing method based on denoising autoe...
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06/11/2020
Deep Structural Causal Models for Tractable Counterfactual Inference
We formulate a general framework for building structural causal models (...
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06/10/2020
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
In image segmentation, there is often more than one plausible solution f...
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01/06/2020
Unpaired Multi-modal Segmentation via Knowledge Distillation
Multi-modal learning is typically performed with network architectures c...
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12/17/2019
Causality matters in medical imaging
This article discusses how the language of causality can shed new light ...
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11/23/2019
Universal Adversarial Perturbations to Understand Robustness of Texture vs. Shape-biased Training
Convolutional Neural Networks (CNNs) used on image classification tasks ...
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10/29/2019