Visual Feature Attribution using Wasserstein GANs

Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding hidden effects in the data. In recent years, approaches based on interpreting a previously trained neural network classifier have become the de facto state-of-the-art and are commonly used on medical as well as natural image datasets. In this paper, we discuss a limitation of these approaches which may lead to only a subset of the category specific features being detected. To address this problem we develop a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN), which does not suffer from this limitation. We show that our proposed method performs substantially better than the state-of-the-art for visual attribution on a synthetic dataset and on real 3D neuroimaging data from patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). For AD patients the method produces compellingly realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201

Adding PDA for Print? Consider your Options for Implementation

Drake University decided to expand our electronic patron-driven acquisition (PDA) program to include print. The reasons were low usage of approval books, librarian uncertainty about which slips to purchase, a desire to make more efficient usage of acquisition funds, and our desire to determine if PDA was a workable acquisitions model for print materials. This paper will discuss the factors the Library considered in selecting a vendor, including the ability to integrate the two formats without duplication, technical considerations, and real-time stock availability to enable rush delivery. Additionally, the paper will discuss librarian and teaching faculty roles in developing PDA profiles and profile considerations (e.g., selection of format, delay in electronic publication, and costs). Drake selected the vendor Ingram’s Coutts to implement the pilot. This paper will discuss and compare Drake\u27s approach to print PDA with other customer, and share details of the choices libraries have when establishing a print PDA plan (determining which titles should be included in the PDA, mediated versus direct to vendor ordering, collecting information about the requesting patron, stock check and rush delivery, etc.). These comparisons will show how the choices made by Drake in setting up the plan and integrating it into the catalog make this print PDA a great example of best practices for others to follow. Finally, this paper will discuss the metrics for determining the success of the project and future considerations, including refining existing profiles, expanding subject areas, budget impact, and developing a weeding method for records in the catalog

Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?

While deep neural network models offer unmatched classification performance, they are prone to learning spurious correlations in the data. Such dependencies on confounding information can be difficult to detect using performance metrics if the test data comes from the same distribution as the training data. Interpretable ML methods such as post-hoc explanations or inherently interpretable classifiers promise to identify faulty model reasoning. However, there is mixed evidence whether many of these techniques are actually able to do so. In this paper, we propose a rigorous evaluation strategy to assess an explanation technique's ability to correctly identify spurious correlations. Using this strategy, we evaluate five post-hoc explanation techniques and one inherently interpretable method for their ability to detect three types of artificially added confounders in a chest x-ray diagnosis task. We find that the post-hoc technique SHAP, as well as the inherently interpretable Attri-Net provide the best performance and can be used to reliably identify faulty model behavior

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.Comment: 12 pages, 8 figures, published in IEEE Transactions in Medical Imagin

Preclinical Testing of Boron-Doped Diamond Electrodes for Root Canal Disinfection—A Series of Preliminary Studies

While numerous approaches have meanwhile been described, sufficient disinfection of root canals is still challenging, mostly due to limited access and the porous structure of dentin. Instead of using different rinsing solutions and activated irrigation, the electrolysis of saline using boron-doped diamond (BDD) electrodes thereby producing reactive oxygen species may be an alternative approach. In a first step, experiments using extracted human teeth incubated with multispecies bacterial biofilm were conducted. The charge quantities required for electrochemical disinfection of root canals were determined, which were subsequently applied in an animal trial using an intraoral canine model. It could be shown that also under realistic clinical conditions, predictable disinfection of root canals could be achieved using BDD electrodes. The parameters required are in the range of 5.5 to 7.0 V and 9 to 38 mA, applied for 2.5 to 6.0 min with approximately 5 to 8 mL of saline. The direct generation of disinfective agents inside the root canal seems to be advantageous especially in situations with compromised access and limited canal sizes. The biologic effect with respect to the host reaction on BDD-mediated disinfection is yet to be examined

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation

Factors associated with deep sternal wound infection after open-heart surgery in a Danish registry

Objective: To conduct a comprehensive multivariate analysis of variables associated with deep sternal wound infection, after open-heart surgery via median sternotomy. Method: A retrospective cohort of all adult patients, who underwent open-heart surgery at Odense University Hospital between 01‐01-2000 and 31-12-2020 was extracted from the West Danish Heart Registry. Data were analyzed using maximum likelihood logistic regression. Results: A total of 15,424 patients underwent open-heart surgery and 244 developed a deep sternal wound infection, equivalent to 1,58 %. After data review 11,182 entries were included in the final analysis, of which 189 developed DSWI, equivalent to 1,69 %. Multivariate analysis found the following variables to be associated with the development of deep sternal wound infection (odds ratios and 95%confidens intervals in parentheses): Known arrhythmia (1.70; 1.16–2.44), Left Ventricular Ejection Fraction (1.66; 1.02–2.58), Body Mass Index 25–30 (1.66; 1.12–2.52), Body Mass Index 30–35 (2.35; 1.50–3.71), Body Mass Index 35–40 (3.61; 2.01–6.33), Body Mass Index 40+ (3.70; 1.03–10.20), Age 60–69 (1.64; 1.04–2.67), Age 70–79 (1.95; 1.23–3.19), Chronic Obstructive Pulmonary Disease (1.77; 1.21–2.54), Reoperation (1.63; 1.06–2.45), Blood transfusion in surgery (1.09; 1.01–1.17), Blood transfusion in intensive care unit (1.03; 1.01–1.06), Known peripheral atherosclerosis (1.82; 1.25–2.61), Current smoking (1.69; 1.20–2.35), Duration of intubation (1.33; 1.12–1.57). Conclusion: Increased risk of deep sternal wound infection after open-heart surgery is a multifactorial problem, while some variables are unchangeable others are not. Focus should be on optimizing the condition of the patient prior to surgery e.g. weight loss and smoking. But also factors surrounding the patient e.g. preventing blood loss and minimizing intubation time.</p

Differential Palmit(e)oylation of Wnt1 on C93 and S224 Residues Has Overlapping and Distinct Consequences

Though the mechanisms by which cytosolic/intracellular proteins are regulated by the post-translational addition of palmitate adducts is well understood, little is known about how this lipid modification affects secreted ligands, such as Wnts. Here we use mutational analysis to show that differential modification of the two known palmit(e)oylated residues of Wnt1, C93 and S224, has both overlapping and distinct consequences. Though the relative roles of each residue are similar with respect to stability and secretion, two distinct biological assays in L cells show that modification of C93 primarily modulates signaling via a ß-catenin independent pathway while S224 is crucial for ß-catenin dependent signaling. In addition, pharmacological inhibition of Porcupine (Porcn), an upstream regulator of Wnt, by IWP1, specifically inhibited ß-catenin dependent signaling. Consistent with these observations, mapping of amino acids in peptide domains containing C93 and S224 demonstrate that acylation of C93 is likely to be Porcn-independent while that of S224 is Porcn-dependent. Cumulatively, our data strongly suggest that C93 and S224 are modified by distinct enzymes and that the differential modification of these sites has the potential to influence Wnt signaling pathway choice

Explaining the Path and Pace of Nuclear Weapons Programs.

This dissertation explores two key questions related to nuclear weapons programs: First, under what conditions do states decide to start nuclear weapons programs? And second, once states begin such programs, when and why do they vary the path and pace of their nuclear development? A state’s wealth and resources, and willingness to make the political decision to begin a nuclear weapons program, determine whether a government will decide to start down the path to nuclear weapons. Past scholarship has identified several different possible factors that increase a state’s risk of making the decision to start such a program. Using event history analysis, and including every country in the world starting in 1939, I demonstrate that elements of the security environment - particularly whether a state has a nuclear strategic rival - and prior nuclear reactor experience have the greatest effect on a state’s decision to start a program. Once a state begins a nuclear weapons program, what affects the path and pace of that program? I offer two main theories regarding this question and make the first scholarly attempt to model the paths of all nuclear weapons programs that have ever existed. I use event history models to conduct the statistical analysis. The models confirm both theories: First, the weaker a state’s civilian control over the military is, the less likely a state will be to accelerate the pace of its program. Second, the more independent a state’s nuclear bureaucracy is, the more likely a state will be to accelerate its nuclear weapons program. Finally, I explore the mechanisms underlying these theories in three historical case studies.PhDPolitical ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110418/1/llkoch_1.pd