b'Ben Glocker'

research

∙ 09/19/2023

Analysing race and sex bias in brain age prediction

Brain age prediction from MRI has become a popular imaging biomarker ass...

0 Carolina Piçarra, et al. ∙

research

∙ 08/14/2023

Distance Matters For Improving Performance Estimation Under Covariate Shift

Performance estimation under covariate shift is a crucial component of s...

0 Mélanie Roschewitz, et al. ∙

research

∙ 08/14/2023

Robustness Stress Testing in Medical Image Classification

Deep neural networks have shown impressive performance for image-based d...

0 Mobarakol Islam, et al. ∙

research

∙ 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...

0 Charles Jones, et al. ∙

research

∙ 07/18/2023

Unsupervised Conditional Slot Attention for Object Centric Learning

Extracting object-level representations for downstream reasoning tasks i...

0 Avinash Kori, et al. ∙

research

∙ 07/11/2023

A Causal Ordering Prior for Unsupervised Representation Learning

Unsupervised representation learning with variational inference relies h...

0 Avinash Kori, et al. ∙

research

∙ 07/06/2023

The Role of Subgroup Separability in Group-Fair Medical Image Classification

We investigate performance disparities in deep classifiers. We find that...

0 Charles Jones, et al. ∙

research

∙ 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...

0 Zeju Li, et al. ∙

research

∙ 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 ...

0 Mobarakol Islam, et al. ∙

research

∙ 01/01/2023

Image To Tree with Recursive Prompting

Extracting complex structures from grid-based data is a common key step ...

0 James Batten, et al. ∙

research

∙ 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 ...

0 Mengya Xu, et al. ∙

research

∙ 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...

0 Nairouz Shehata, et al. ∙

research

∙ 10/17/2022

Visual Debates

The natural way of obtaining different perspectives on any given topic i...

17 Avinash Kori, et al. ∙

research

∙ 09/20/2022

Frequency Dropout: Feature-Level Regularization via Randomized Filtering

Deep convolutional neural networks have shown remarkable performance on ...

16 Mobarakol Islam, et al. ∙

research

∙ 09/07/2022

Risk of Bias in Chest X-ray Foundation Models

Foundation models are considered a breakthrough in all applications of A...

7 Ben Glocker, et al. ∙

research

∙ 08/23/2022

Deep Structural Causal Shape Models

Causal reasoning provides a language to ask important interventional and...

22 Rajat Rasal, et al. ∙

research

∙ 07/05/2022

Vector Quantisation for Robust Segmentation

The reliability of segmentation models in the medical domain depends on ...

21 Ainkaran Santhirasekaram, et al. ∙

research

∙ 07/05/2022

GLANCE: Global to Local Architecture-Neutral Concept-based Explanations

Most of the current explainability techniques focus on capturing the imp...

13 Avinash Kori, et al. ∙

research

∙ 07/05/2022

Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models

Explanations for black-box models help us understand model decisions as ...

14 Ainkaran Santhirasekaram, et al. ∙

research

∙ 06/27/2022

Distributional Gaussian Processes Layers for Out-of-Distribution Detection

Machine learning models deployed on medical imaging tasks must be equipp...

18 Sebastian G. Popescu, et al. ∙

research

∙ 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...

58 Melanie Bernhardt, et al. ∙

research

∙ 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...

27 Melanie Bernhardt, et al. ∙

research

∙ 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...

5 Reuben Dorent, et al. ∙

research

∙ 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...

8 Ben Glocker, et al. ∙

research

∙ 10/08/2021

Is MC Dropout Bayesian?

MC Dropout is a mainstream "free lunch" method in medical imaging for ap...

8 Loic Le Folgoc, et al. ∙

research

∙ 09/30/2021

DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization

In recent years, the research landscape of machine learning in medical i...

51 Turkay Kart, et al. ∙

research

∙ 09/11/2021

Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition

Despite impressive accuracy, deep neural networks are often miscalibrate...

4 Mobarakol Islam, et al. ∙

research

∙ 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...

8 Vasileios Baltatzis, et al. ∙

research

∙ 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...

16 Vasileios Baltatzis, et al. ∙

research

∙ 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. ...

0 Loic Le Folgoc, et al. ∙

research

∙ 07/20/2021

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions

Despite technological and medical advances, the detection, interpretatio...

23 Richard Osuala, et al. ∙

research

∙ 07/19/2021

Transductive image segmentation: Self-training and effect of uncertainty estimation

Semi-supervised learning (SSL) uses unlabeled data during training to le...

11 Konstantinos Kamnitsas, et al. ∙

research

∙ 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...

14 Talha Qaiser, et al. ∙

research

∙ 07/13/2021

Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift

Scarcity of high quality annotated images remains a limiting factor for ...

3 Gregory Filbrandt, et al. ∙

research

∙ 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...

8 Christoph Berger, et al. ∙

research

∙ 04/28/2021

Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation

We propose a parameter efficient Bayesian layer for hierarchical convolu...

25 Sebastian G. Popescu, et al. ∙

research

∙ 04/12/2021

Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations

The task of image segmentation is inherently noisy due to ambiguities re...

4 Mobarakol Islam, et al. ∙

research

∙ 04/12/2021

Common Limitations of Image Processing Metrics: A Picture Story

While the importance of automatic image analysis is increasing at an eno...

6 Annika Reinke, et al. ∙

research

∙ 02/20/2021

Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation

Class imbalance poses a challenge for developing unbiased, accurate pred...

7 Zeju Li, et al. ∙

research

∙ 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...

6 Matthew Sinclair, et al. ∙

research

∙ 10/28/2020

Hierarchical Gaussian Processes with Wasserstein-2 Kernels

We investigate the usefulness of Wasserstein-2 kernels in the context of...

0 Sebastian Popescu, et al. ∙

research

∙ 09/29/2020

Cranial Implant Design via Virtual Craniectomy with Shape Priors

Cranial implant design is a challenging task, whose accuracy is crucial ...

8 Franco Matzkin, et al. ∙

research

∙ 07/07/2020

Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy

Decompressive craniectomy (DC) is a common surgical procedure consisting...

13 Franco Matzkin, et al. ∙

research

∙ 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...

1 Agostina J. Larrazabal, et al. ∙

research

∙ 06/11/2020

Deep Structural Causal Models for Tractable Counterfactual Inference

We formulate a general framework for building structural causal models (...

48 Nick Pawlowski, et al. ∙

research

∙ 06/10/2020

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

In image segmentation, there is often more than one plausible solution f...

32 Miguel Monteiro, et al. ∙

research

∙ 01/06/2020

Unpaired Multi-modal Segmentation via Knowledge Distillation

Multi-modal learning is typically performed with network architectures c...

23 Qi Dou, et al. ∙

research

∙ 12/17/2019

Causality matters in medical imaging

This article discusses how the language of causality can shed new light ...

24 Daniel Coelho de Castro, et al. ∙

research

∙ 11/23/2019

Universal Adversarial Perturbations to Understand Robustness of Texture vs. Shape-biased Training

Convolutional Neural Networks (CNNs) used on image classification tasks ...

18 CK, et al. ∙

research

∙ 10/29/2019

Domain Generalization via Model-Agnostic Learning of Semantic Features

Generalization capability to unseen domains is crucial for machine learn...

21 Qi Dou, et al. ∙

Ben Glocker

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