Perinatal Depression: Breaking Barriers to Treatment

Depression in the perinatal period is a common medical issue in Vermont, affecting about 10% of women. Multiple and severe consequences of depression during this time are seen for both mothers and babies, including lower breastfeeding rates, fewer healthcare visits for the child, and psychopathology in the child later on. The goal of this project is to identify and address some of the barriers we currently face in identifying and treating women for depression. Major barriers women encounter in seeking help involve poor recognition of symptoms facing increasing stress of motherhood, stigma, as well as neglecting to attend to mental health preemptively. An educational pamphlet for mothers was developed to address these issues.https://scholarworks.uvm.edu/fmclerk/1439/thumbnail.jp

The split-and-drift random graph, a null model for speciation

We introduce a new random graph model motivated by biological questions relating to speciation. This random graph is defined as the stationary distribution of a Markov chain on the space of graphs on $\{1, \ldots, n\}$. The dynamics of this Markov chain is governed by two types of events: vertex duplication, where at constant rate a pair of vertices is sampled uniformly and one of these vertices loses its incident edges and is rewired to the other vertex and its neighbors; and edge removal, where each edge disappears at constant rate. Besides the number of vertices $n$, the model has a single parameter $r_n$. Using a coalescent approach, we obtain explicit formulas for the first moments of several graph invariants such as the number of edges or the number of complete subgraphs of order $k$. These are then used to identify five non-trivial regimes depending on the asymptotics of the parameter $r_n$. We derive an explicit expression for the degree distribution, and show that under appropriate rescaling it converges to classical distributions when the number of vertices goes to infinity. Finally, we give asymptotic bounds for the number of connected components, and show that in the sparse regime the number of edges is Poissonian.Comment: added Proposition 2.4 and formal proofs of Proposition 2.3 and 2.

TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images

The volume of a brain lesion (e.g. infarct or tumor) is a powerful indicator of patient prognosis and can be used to guide the therapeutic strategy. Lesional volume estimation is usually performed by segmentation with deep convolutional neural networks (CNN), currently the state-of-the-art approach. However, to date, few work has been done to equip volume segmentation tools with adequate quantitative predictive intervals, which can hinder their usefulness and acceptation in clinical practice. In this work, we propose TriadNet, a segmentation approach relying on a multi-head CNN architecture, which provides both the lesion volumes and the associated predictive intervals simultaneously, in less than a second. We demonstrate its superiority over other solutions on BraTS 2021, a large-scale MRI glioblastoma image database.Comment: Accepted for presentation at the Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) at MICCAI 202

Multi-layer Aggregation as a key to feature-based OOD detection

Deep Learning models are easily disturbed by variations in the input images that were not observed during the training stage, resulting in unpredictable predictions. Detecting such Out-of-Distribution (OOD) images is particularly crucial in the context of medical image analysis, where the range of possible abnormalities is extremely wide. Recently, a new category of methods has emerged, based on the analysis of the intermediate features of a trained model. These methods can be divided into 2 groups: single-layer methods that consider the feature map obtained at a fixed, carefully chosen layer, and multi-layer methods that consider the ensemble of the feature maps generated by the model. While promising, a proper comparison of these algorithms is still lacking. In this work, we compared various feature-based OOD detection methods on a large spectra of OOD (20 types), representing approximately 7800 3D MRIs. Our experiments shed the light on two phenomenons. First, multi-layer methods consistently outperform single-layer approaches, which tend to have inconsistent behaviour depending on the type of anomaly. Second, the OOD detection performance highly depends on the architecture of the underlying neural network.Comment: Accepted for presentation at the Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) at MICCAI 202

Radon, From the Ground into Our Schools: Parent/Guardian Awareness of Radon Levels in Vermont Schools

Introduction. Radon is the leading cause of lung cancer among non-smokers. Ex- posure to radon in schools may be harmful to schoolchildren, faculty, and staff, but there is currently no legislation mandating testing or mitigation of radon levels in Vermont schools. Objectives. The goal of our study was to assess Vermont parents’ awareness of radon’s harmful effects, as well as awareness of and support for testing and mitigation of radon levels in their children’s schools. Methods. We distributed paper and online surveys to Vermont parents of children grades K-12. 126 surveys were received and quantitatively analyzed. We held a focus group of two Vermont parents to gather qualitative data. Results. Most surveyed parents demonstrated general knowledge of radon, but only 51% believed that radon affects the lungs. 8% were confident that their children’s schools had informed them about radon levels. 91.2% believe their children’s schools should take action to address elevated radon levels and 87% would support mandated mitigation. There is some concern and lack of knowledge about the financial implications of radon mitigation. Conclusions. Most Vermont parents of children grades K-12 are unaware that radon is a lung carcinogen and do not know their children’s school’s radon levels or mitigation status. However, most are in favor of legislation that would require testing and dis- closure of schools’ high radon levels. Educating parents about school radon levels and their association with lung cancer could be a foundation for community support of legislation that mandates testing and mitigation of radon in Vermont schools.https://scholarworks.uvm.edu/comphp_gallery/1252/thumbnail.jp

Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust

Deep neural networks have become the gold-standard approach for the automated segmentation of 3D medical images. Their full acceptance by clinicians remains however hampered by the lack of intelligible uncertainty assessment of the provided results. Most approaches to quantify their uncertainty, such as the popular Monte Carlo dropout, restrict to some measure of uncertainty in prediction at the voxel level. In addition not to be clearly related to genuine medical uncertainty, this is not clinically satisfying as most objects of interest (e.g. brain lesions) are made of groups of voxels whose overall relevance may not simply reduce to the sum or mean of their individual uncertainties. In this work, we propose to go beyond voxel-wise assessment using an innovative Graph Neural Network approach, trained from the outputs of a Monte Carlo dropout model. This network allows the fusion of three estimators of voxel uncertainty: entropy, variance, and model's confidence; and can be applied to any lesion, regardless of its shape or size. We demonstrate the superiority of our approach for uncertainty estimate on a task of Multiple Sclerosis lesions segmentation.Comment: Accepted for presentation at the Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC) at MICCAI 202

Introduction

Le point de départ de cette journée a été la rencontre entre l’interrogation comparatiste de Florence Goyet et celle des spécialistes de la Sibérie autour du genre épique. Tous les peuples sibériens n’ont pas développé une poésie épique, bien loin de là, mais on parle habituellement d’épopées dans six grands ensembles : Nord-Samoyèdes (Nénètses, Énètses, Nganassanes), Ougriens de l’Ob (Khantes et Mansis), Bouriates, Turcs de Sibérie méridionale (Altaïens, Chors, Touvas, Khakasses), Yakoutes (..

Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated to DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their quality variability, as well as constraints associated to real-life clinical routine. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges of uncertainty quantification in the medical field