That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation

Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set. However, in practice these annotations can differ systematically in the way they are generated, for example through the use of different labeling tools. This results in datasets that contain both data variability and differing label styles. In this paper, we demonstrate that applying state-of-the-art segmentation uncertainty models on such datasets can lead to model bias caused by the different label styles. We present an updated modelling objective conditioning on labeling style for aleatoric uncertainty estimation, and modify two state-of-the-art-architectures for segmentation uncertainty accordingly. We show with extensive experiments that this method reduces label style bias, while improving segmentation performance, increasing the applicability of segmentation uncertainty models in the wild. We curate two datasets, with annotations in different label styles, which we will make publicly available along with our code upon publication.

Removing confounding information from fetal ultrasound images

Confounding information in the form of text or markings embedded in medical images can severely affect the training of diagnostic deep learning algorithms. However, data collected for clinical purposes often have such markings embedded in them. In dermatology, known examples include drawings or rulers that are overrepresented in images of malignant lesions. In this paper, we encounter text and calipers placed on the images found in national databases containing fetal screening ultrasound scans, which correlate with standard planes to be predicted. In order to utilize the vast amounts of data available in these databases, we develop and validate a series of methods for minimizing the confounding effects of embedded text and calipers on deep learning algorithms designed for ultrasound, using standard plane classification as a test case.

Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty

Out of distribution (OOD) medical images are frequently encountered, e.g. because of site- or scanner differences, or image corruption. OOD images come with a risk of incorrect image segmentation, potentially negatively affecting downstream diagnoses or treatment. To ensure robustness to such incorrect segmentations, we propose Laplacian Segmentation Networks (LSN) that jointly model epistemic (model) and aleatoric (data) uncertainty in image segmentation. We capture data uncertainty with a spatially correlated logit distribution. For model uncertainty, we propose the first Laplace approximation of the weight posterior that scales to large neural networks with skip connections that have high-dimensional outputs. Empirically, we demonstrate that modelling spatial pixel correlation allows the Laplacian Segmentation Network to successfully assign high epistemic uncertainty to out-of-distribution objects appearing within images.

On (assessing) the fairness of risk score models

Recent work on algorithmic fairness has largely focused on the fairness of discrete decisions, or classifications. While such decisions are often based on risk score models, the fairness of the risk models themselves has received considerably less attention. Risk models are of interest for a number of reasons, including the fact that they communicate uncertainty about the potential outcomes to users, thus representing a way to enable meaningful human oversight. Here, we address fairness desiderata for risk score models. We identify the provision of similar epistemic value to different groups as a key desideratum for risk score fairness. Further, we address how to assess the fairness of risk score models quantitatively, including a discussion of metric choices and meaningful statistical comparisons between groups. In this context, we also introduce a novel calibration error metric that is less sample size-biased than previously proposed metrics, enabling meaningful comparisons between groups of different sizes. We illustrate our methodology - which is widely applicable in many other settings - in two case studies, one in recidivism risk prediction, and one in risk of major depressive disorder (MDD) prediction.

I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks

Concept bottleneck models (CBMs) include a bottleneck of human-interpretable concepts providing explainability and intervention during inference by correcting the predicted, intermediate concepts. This makes CBMs attractive for high-stakes decision-making. In this paper, we take the quality assessment of fetal ultrasound scans as a real-life use case for CBM decision support in healthcare. For this case, simple binary concepts are not sufficiently reliable, as they are mapped directly from images of highly variable quality, for which variable model calibration might lead to unstable binarized concepts. Moreover, scalar concepts do not provide the intuitive spatial feedback requested by users. To address this, we design a hierarchical CBM imitating the sequential expert decision-making process of "seeing", "conceiving" and "concluding". Our model first passes through a layer of visual, segmentation-based concepts, and next a second layer of property concepts directly associated with the decision-making task. We note that experts can intervene on both the visual and property concepts during inference. Additionally, we increase the bottleneck capacity by considering task-relevant concept interaction. Our application of ultrasound scan quality assessment is challenging, as it relies on balancing the (often poor) image quality against an assessment of the visibility and geometric properties of standardized image content. Our validation shows that -- in contrast with previous CBM models -- our CBM models actually outperform equivalent concept-free models in terms of predictive performance. Moreover, we illustrate how interventions can further improve our performance over the state-of-the-art.

DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation

Curvilinear structure segmentation plays an important role in many applications. The standard formulation of segmentation as pixel-wise classification often fails to capture these structures due to the small size and low contrast. Some works introduce prior topological information to address this problem with the cost of expensive computations and the need for extra labels. Moreover, prior work primarily focuses on avoiding false splits by encouraging the connection of small gaps. Less attention has been given to avoiding missed splits, namely the incorrect inference of structures that are not visible in the image. In this paper, we present DTU-Net, a dual-decoder and topology-aware deep neural network consisting of two sequential light-weight U-Nets, namely a texture net, and a topology net. The texture net makes a coarse prediction using image texture information. The topology net learns topological information from the coarse prediction by employing a triplet loss trained to recognize false and missed splits, and provides a topology-aware separation of the foreground and background. The separation is further utilized to correct the coarse prediction. We conducted experiments on a challenging multi-class ultrasound scan segmentation dataset and an open dataset for road extraction. Results show that our model achieves state-of-the-art results in both segmentation accuracy and continuity. Compared to existing methods, our model corrects both false positive and false negative examples more effectively with no need for prior knowledge.

Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection

Convolutional neural networks have enabled significant improvements in medical image-based disease classification. It has, however, become increasingly clear that these models are susceptible to performance degradation due to spurious correlations and dataset shifts, which may lead to underperformance on underrepresented patient groups, among other problems. In this paper, we compare two classification schemes on the ADNI MRI dataset: a very simple logistic regression model that uses manually selected volumetric features as inputs, and a convolutional neural network trained on 3D MRI data. We assess the robustness of the trained models in the face of varying dataset splits, training set sex composition, and stage of disease. In contrast to earlier work on diagnosing lung diseases based on chest x-ray data, we do not find a strong dependence of model performance for male and female test subjects on the sex composition of the training dataset. Moreover, in our analysis, the low-dimensional model with manually selected features outperforms the 3D CNN, thus emphasizing the need for automatic robust feature extraction methods and the value of manual feature specification (based on prior knowledge) for robustness.

diffConv: Analyzing Irregular Point Clouds with an Irregular View

Standard spatial convolutions assume input data with a regular neighborhood structure. Existing methods typically generalize convolution to the irregular point cloud domain by fixing a regular "view" through e.g. a fixed neighborhood size, where the convolution kernel size remains the same for each point. However, since point clouds are not as structured as images, the fixed neighbor number gives an unfortunate inductive bias. We present a novel graph convolution named Difference Graph Convolution (diffConv), which does not rely on a regular view. diffConv operates on spatially-varying and density-dilated neighborhoods, which are further adapted by a learned masked attention mechanism. We validate our model on the ModelNet40 point cloud classification benchmark, obtaining state-of-the-art performance and far more robustness to noise, along with a much faster inference speed.

Spot the Difference: Topological Anomaly Detection via Geometric Alignment

Geometric alignment appears in a variety of applications, ranging from domain adaptation, optimal transport, and normalizing flows in machine learning; optical flow and learned augmentation in computer vision and deformable registration within biomedical imaging. A recurring challenge is the alignment of domains whose topology is not the same; a problem that is routinely ignored, potentially introducing bias in downstream analysis. As a first step towards solving such alignment problems, we propose an unsupervised topological difference detection algorithm. The model is based on a conditional variational auto-encoder and detects topological anomalies with regards to a reference alongside the registration step. We consider both a) topological changes in the image under spatial variation and b) unexpected transformations. Our approach is validated on a proxy task of unsupervised anomaly detection in images.