52 research outputs found
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation
(SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of
automatically identifying pathologies in brain images. Our work challenges the
effectiveness of current Machine Learning (ML) approaches in this application
domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR)
MR scans provides better anomaly segmentation maps than several different
ML-based anomaly detection models. Specifically, our method achieves better
Dice similarity coefficients and Precision-Recall curves than the competitors
on various popular evaluation data sets for the segmentation of tumors and
multiple sclerosis lesions.Comment: 10 pages, 4 figures, accepted to the MICCAI 2021 BrainLes Worksho
Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis
Machine learning with formal privacy-preserving techniques like Differential
Privacy (DP) allows one to derive valuable insights from sensitive medical
imaging data while promising to protect patient privacy, but it usually comes
at a sharp privacy-utility trade-off. In this work, we propose to use steerable
equivariant convolutional networks for medical image analysis with DP. Their
improved feature quality and parameter efficiency yield remarkable accuracy
gains, narrowing the privacy-utility gap.Comment: Accepted as extended abstract at GeoMedIA Workshop 2022
(https://openreview.net/forum?id=rGYfMrMxI17
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning
Normalization is an important but understudied challenge in privacy-related
application domains such as federated learning (FL), differential privacy (DP),
and differentially private federated learning (DP-FL). While the unsuitability
of batch normalization for these domains has already been shown, the impact of
other normalization methods on the performance of federated or differentially
private models is not well-known. To address this, we draw a performance
comparison among layer normalization (LayerNorm), group normalization
(GroupNorm), and the recently proposed kernel normalization (KernelNorm) in FL,
DP, and DP-FL settings. Our results indicate LayerNorm and GroupNorm provide no
performance gain compared to the baseline (i.e. no normalization) for shallow
models in FL and DP. They, on the other hand, considerably enhance the
performance of shallow models in DP-FL and deeper models in FL and DP.
KernelNorm, moreover, significantly outperforms its competitors in terms of
accuracy and convergence rate (or communication efficiency) for both shallow
and deeper models in all considered learning environments. Given these key
observations, we propose a kernel normalized ResNet architecture called
KNResNet-13 for differentially private learning. Using the proposed
architecture, we provide new state-of-the-art accuracy values on the CIFAR-10
and Imagenette datasets, when trained from scratch.Comment: To appear in the IEEE Conference on Secure and Trustworthy Machine
Learning (SaTML), February 202
Kernel Normalized Convolutional Networks
Existing deep convolutional neural network (CNN) architectures frequently
rely upon batch normalization (BatchNorm) to effectively train the model.
BatchNorm significantly improves model performance in centralized training, but
it is unsuitable for federated learning and differential privacy settings. Even
in centralized learning, BatchNorm performs poorly with smaller batch sizes. To
address these limitations, we propose kernel normalization and kernel
normalized convolutional layers, and incorporate them into kernel normalized
convolutional networks (KNConvNets) as the main building blocks. We implement
KNConvNets corresponding to the state-of-the-art CNNs such as VGGNets and
ResNets while forgoing BatchNorm layers. Through extensive experiments, we
illustrate KNConvNets consistently outperform their batch, group, and layer
normalized counterparts in terms of both accuracy and convergence rate in
centralized, federated, and differentially private learning settings
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Deep unsupervised approaches are gathering increased attention for
applications such as pathology detection and segmentation in medical images
since they promise to alleviate the need for large labeled datasets and are
more generalizable than their supervised counterparts in detecting any kind of
rare pathology. As the Unsupervised Anomaly Detection (UAD) literature
continuously grows and new paradigms emerge, it is vital to continuously
evaluate and benchmark new methods in a common framework, in order to reassess
the state-of-the-art (SOTA) and identify promising research directions. To this
end, we evaluate a diverse selection of cutting-edge UAD methods on multiple
medical datasets, comparing them against the established SOTA in UAD for brain
MRI. Our experiments demonstrate that newly developed feature-modeling methods
from the industrial and medical literature achieve increased performance
compared to previous work and set the new SOTA in a variety of modalities and
datasets. Additionally, we show that such methods are capable of benefiting
from recently developed self-supervised pre-training algorithms, further
increasing their performance. Finally, we perform a series of experiments in
order to gain further insights into some unique characteristics of selected
models and datasets. Our code can be found under
https://github.com/iolag/UPD_study/.Comment: 12 pages, 4 figures, accepted for publication in IEEE Transactions on
Medical Imaging (added copyright, DOI information
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
We explore Reconstruction Robustness (ReRo), which was recently proposed as
an upper bound on the success of data reconstruction attacks against machine
learning models. Previous research has demonstrated that differential privacy
(DP) mechanisms also provide ReRo, but so far, only asymptotic Monte Carlo
estimates of a tight ReRo bound have been shown. Directly computable ReRo
bounds for general DP mechanisms are thus desirable. In this work, we establish
a connection between hypothesis testing DP and ReRo and derive closed-form,
analytic or numerical ReRo bounds for the Laplace and Gaussian mechanisms and
their subsampled variants
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
Unsupervised Anomaly Detection has become a popular method to detect
pathologies in medical images as it does not require supervision or labels for
training. Most commonly, the anomaly detection model generates a "normal"
version of an input image, and the pixel-wise -difference of the two is
used to localize anomalies. However, large residuals often occur due to
imperfect reconstruction of the complex anatomical structures present in most
medical images. This method also fails to detect anomalies that are not
characterized by large intensity differences to the surrounding tissue. We
propose to tackle this problem using a feature-mapping function that transforms
the input intensity images into a space with multiple channels where anomalies
can be detected along different discriminative feature maps extracted from the
original image. We then train an Autoencoder model in this space using
structural similarity loss that does not only consider differences in intensity
but also in contrast and structure. Our method significantly increases
performance on two medical data sets for brain MRI. Code and experiments are
available at https://github.com/FeliMe/feature-autoencoderComment: 10 pages, 5 figures, one table, accepted to the MICCAI 2021 BrainLes
Worksho
Interactive and Explainable Region-guided Radiology Report Generation
The automatic generation of radiology reports has the potential to assist
radiologists in the time-consuming task of report writing. Existing methods
generate the full report from image-level features, failing to explicitly focus
on anatomical regions in the image. We propose a simple yet effective
region-guided report generation model that detects anatomical regions and then
describes individual, salient regions to form the final report. While previous
methods generate reports without the possibility of human intervention and with
limited explainability, our method opens up novel clinical use cases through
additional interactive capabilities and introduces a high degree of
transparency and explainability. Comprehensive experiments demonstrate our
method's effectiveness in report generation, outperforming previous
state-of-the-art models, and highlight its interactive capabilities. The code
and checkpoints are available at https://github.com/ttanida/rgrg .Comment: Accepted at CVPR 202
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