Robust Detection Outcome: A Metric for Pathology Detection in Medical Images

Detection of pathologies is a fundamental task in medical imaging and the evaluation of algorithms that can perform this task automatically is crucial. However, current object detection metrics for natural images do not reflect the specific clinical requirements in pathology detection sufficiently. To tackle this problem, we propose Robust Detection Outcome (RoDeO); a novel metric for evaluating algorithms for pathology detection in medical images, especially in chest X-rays. RoDeO evaluates different errors directly and individually, and reflects clinical needs better than current metrics. Extensive evaluation on the ChestX-ray8 dataset shows the superiority of our metrics compared to existing ones. We released the code at https://github.com/FeliMe/RoDeO and published RoDeO as pip package (rodeometric).

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/.

Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology

Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure the protection of said data are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. However, prior work has shown that DP has negative implications on model accuracy and fairness. Therefore, the purpose of this study is to demonstrate that the privacy-preserving training of AI models for chest radiograph diagnosis is possible with high accuracy and fairness compared to non-private training. N=193,311 high quality clinical chest radiographs were retrospectively collected and manually labeled by experienced radiologists, who assigned one or more of the following diagnoses: cardiomegaly, congestion, pleural effusion, pneumonic infiltration and atelectasis, to each side (where applicable). The non-private AI models were compared with privacy-preserving (DP) models with respect to privacy-utility trade-offs (measured as area under the receiver-operator-characteristic curve (AUROC)), and privacy-fairness trade-offs (measured as Pearson-R or Statistical Parity Difference). The non-private AI model achieved an average AUROC score of 0.90 over all labels, whereas the DP AI model with a privacy budget of epsilon=7.89 resulted in an AUROC of 0.87, i.e., a mere 2.6% performance decrease compared to non-private training. The privacy-preserving training of diagnostic AI models can achieve high performance with a small penalty on model accuracy and does not amplify discrimination against age, sex or co-morbidity. We thus encourage practitioners to integrate state-of-the-art privacy-preserving techniques into medical AI model development.

Equivariant Differentially Private Deep Learning

The formal privacy guarantee provided by Differential Privacy (DP) bounds the leakage of sensitive information from deep learning models. In practice, however, this comes at a severe computation and accuracy cost. The recently established state of the art (SOTA) results in image classification under DP are due to the use of heavy data augmentation and large batch sizes, leading to a drastically increased computation overhead. In this work, we propose to use more efficient models with improved feature quality by introducing steerable equivariant convolutional networks for DP training. We demonstrate that our models are able to outperform the current SOTA performance on CIFAR-10 by up to $9\%$ across different $\varepsilon$-values while reducing the number of model parameters by a factor of $35$ and decreasing the computation time by more than $90 \%$. Our results are a large step towards efficient model architectures that make optimal use of their parameters and bridge the privacy-utility gap between private and non-private deep learning for computer vision.

How Do Input Attributes Impact the Privacy Loss in Differential Privacy?

Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database. More recently, extensions to individual subjects or their attributes, have been introduced. Under the individual/per-instance DP interpretation, we study the connection between the per-subject gradient norm in DP neural networks and individual privacy loss and introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS), which allows one to apportion the subject's privacy loss to their input attributes. We experimentally show how this enables the identification of sensitive attributes and of subjects at high risk of data reconstruction.

The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images

Image-text contrastive learning has proven effective for pretraining medical image models. When targeting localized downstream tasks like semantic segmentation or object detection, additional local contrastive losses that align image regions with sentences have shown promising results. We study how local contrastive losses are related to global (per-sample) contrastive losses and which effects they have on localized medical downstream tasks. Based on a theoretical comparison, we propose to remove some components of local losses and replace others by a novel distribution prior which enforces uniformity of representations within each sample. We empirically study this approach on chest X-ray tasks and find it to be very effective, outperforming methods without local losses on 12 of 18 tasks.

Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients

The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural networks able to tackle this challenge is impeded by a lack of large datasets and the difficult anatomical localisation of the pancreas. Here, we propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy which is based on the Response Evaluation Criteria in Solid Tumors (RECIST) score, a standardised method for cancer response evaluation by clinicians as well as tumour markers, and clinical evaluation of the patients. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total.

Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens

Differential Privacy (DP) provides tight upper bounds on the capabilities of optimal adversaries, but such adversaries are rarely encountered in practice. Under the hypothesis testing/membership inference interpretation of DP, we examine the Gaussian mechanism and relax the usual assumption of a Neyman-Pearson-Optimal (NPO) adversary to a Generalized Likelihood Test (GLRT) adversary. This mild relaxation leads to improved privacy guarantees, which we express in the spirit of Gaussian DP and $(\varepsilon, \delta)$-DP, including composition and sub-sampling results. We evaluate our results numerically and find them to match the theoretical upper bounds.

Label Noise-Robust Learning using a Confidence-Based Sieving Strategy

In learning tasks with label noise, boosting model robustness against overfitting is a pivotal challenge because the model eventually memorizes labels including the noisy ones. Identifying the samples with corrupted labels and preventing the model from learning them is a promising approach to address this challenge. Per-sample training loss is a previously studied metric that considers samples with small loss as clean samples on which the model should be trained. In this work, we first demonstrate the ineffectiveness of this small-loss trick. Then, we propose a novel discriminator metric called confidence error and a sieving strategy called CONFES to effectively differentiate between the clean and noisy samples. We experimentally illustrate the superior performance of our proposed approach compared to recent studies on various settings such as synthetic and real-world label noise.

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) and differential privacy (DP). While the unsuitability of batch normalization for FL and DP has already been shown, the impact of the 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 and DP settings. Our results indicate LayerNorm and GroupNorm provide no performance gain compared to the baseline (i.e. no normalization) for shallow models, but they considerably enhance performance of deeper models. KernelNorm, on the other hand, significantly outperforms its competitors in terms of accuracy and convergence rate (or communication efficiency) for both shallow and deeper models. Given these key observations, we propose a kernel normalized ResNet architecture called KNResNet-13 for differentially private learning environments. Using the proposed architecture, we provide new state-of-the-art accuracy values on the CIFAR-10 and Imagenette datasets.