280 research outputs found
Frequency Dropout: Feature-Level Regularization via Randomized Filtering
Deep convolutional neural networks have shown remarkable performance on
various computer vision tasks, and yet, they are susceptible to picking up
spurious correlations from the training signal. So called `shortcuts' can occur
during learning, for example, when there are specific frequencies present in
the image data that correlate with the output predictions. Both high and low
frequencies can be characteristic of the underlying noise distribution caused
by the image acquisition rather than in relation to the task-relevant
information about the image content. Models that learn features related to this
characteristic noise will not generalize well to new data.
In this work, we propose a simple yet effective training strategy, Frequency
Dropout, to prevent convolutional neural networks from learning
frequency-specific imaging features. We employ randomized filtering of feature
maps during training which acts as a feature-level regularization. In this
study, we consider common image processing filters such as Gaussian smoothing,
Laplacian of Gaussian, and Gabor filtering. Our training strategy is
model-agnostic and can be used for any computer vision task. We demonstrate the
effectiveness of Frequency Dropout on a range of popular architectures and
multiple tasks including image classification, domain adaptation, and semantic
segmentation using both computer vision and medical imaging datasets. Our
results suggest that the proposed approach does not only improve predictive
accuracy but also improves robustness against domain shift.Comment: 15 page
Analysing race and sex bias in brain age prediction
Brain age prediction from MRI has become a popular imaging biomarker
associated with a wide range of neuropathologies. The datasets used for
training, however, are often skewed and imbalanced regarding demographics,
potentially making brain age prediction models susceptible to bias. We analyse
the commonly used ResNet-34 model by conducting a comprehensive subgroup
performance analysis and feature inspection. The model is trained on 1,215
T1-weighted MRI scans from Cam-CAN and IXI, and tested on UK Biobank
(n=42,786), split into six racial and biological sex subgroups. With the
objective of comparing the performance between subgroups, measured by the
absolute prediction error, we use a Kruskal-Wallis test followed by two
post-hoc Conover-Iman tests to inspect bias across race and biological sex. To
examine biases in the generated features, we use PCA for dimensionality
reduction and employ two-sample Kolmogorov-Smirnov tests to identify
distribution shifts among subgroups. Our results reveal statistically
significant differences in predictive performance between Black and White,
Black and Asian, and male and female subjects. Seven out of twelve pairwise
comparisons show statistically significant differences in the feature
distributions. Our findings call for further analysis of brain age prediction
models.Comment: MICCAI Workshop on Fairness of AI in Medical Imaging (FAIMI 2023
Is Texture Predictive for Age and Sex in Brain MRI?
Deep learning builds the foundation for many medical image analysis tasks
where neuralnetworks are often designed to have a large receptive field to
incorporate long spatialdependencies. Recent work has shown that large
receptive fields are not always necessaryfor computer vision tasks on natural
images. We explore whether this translates to certainmedical imaging tasks such
as age and sex prediction from a T1-weighted brain MRI scans.Comment: MIDL 2019 [arXiv:1907.08612
Distance Matters For Improving Performance Estimation Under Covariate Shift
Performance estimation under covariate shift is a crucial component of safe
AI model deployment, especially for sensitive use-cases. Recently, several
solutions were proposed to tackle this problem, most leveraging model
predictions or softmax confidence to derive accuracy estimates. However, under
dataset shifts, confidence scores may become ill-calibrated if samples are too
far from the training distribution. In this work, we show that taking into
account distances of test samples to their expected training distribution can
significantly improve performance estimation under covariate shift. Precisely,
we introduce a "distance-check" to flag samples that lie too far from the
expected distribution, to avoid relying on their untrustworthy model outputs in
the accuracy estimation step. We demonstrate the effectiveness of this method
on 13 image classification tasks, across a wide-range of natural and synthetic
distribution shifts and hundreds of models, with a median relative MAE
improvement of 27% over the best baseline across all tasks, and SOTA
performance on 10 out of 13 tasks. Our code is publicly available at
https://github.com/melanibe/distance_matters_performance_estimation.Comment: Accepted to ICCV Workshop on Uncertainty Quantification for Computer
Vision 202
- …