Differentiating between integration and non-integration strategies in perceptual decision making.

Many tasks used to study decision-making encourage subjects to integrate evidence over time. Such tasks are useful to understand how the brain operates on multiple samples of information over prolonged timescales, but only if subjects actually integrate evidence to form their decisions. We explored the behavioral observations that corroborate evidence-integration in a number of task-designs. Several commonly accepted signs of integration were also predicted by non-integration strategies. Furthermore, an integration model could fit data generated by non-integration models. We identified the features of non-integration models that allowed them to mimic integration and used these insights to design a motion discrimination task that disentangled the models. In human subjects performing the task, we falsified a non-integration strategy in each and confirmed prolonged integration in all but one subject. The findings illustrate the difficulty of identifying a decision-maker's strategy and support solutions to achieve this goal

A rare case of a patient with PPP syndrome presenting pancreatic pseudocysts, panniculitis, and symptoms of polyarthritis. A radicular cyst of the upper jaw could be another manifestation of the syndrome

Abstract In rare cases, pancreatic enzymes can enter the bloodstream and cause fat necrosis in the bone and tissue leading to a disorder called pancreatitis, panniculitis, and polyarthritis syndrome. Clinicians should have this syndrome in mind when treating patients with pancreatitis

A self-guided anomaly detection-inspired few-shot segmentation network

Source at: https://ceur-ws.org/Vol-3271/Paper18_CVCS2022.pdfStandard strategies for fully supervised semantic segmentation of medical images require large pixel-level annotated datasets. This makes such methods challenging due to the manual labor required and limits the usability when segmentation is needed for new classes for which data is scarce. Few-shot segmentation (FSS) is a recent and promising direction within the deep learning literature designed to alleviate these challenges. In FSS, the aim is to create segmentation networks with the ability to generalize based on just a few annotated examples, inspired by human learning. A dominant direction in FSS is based on matching representations of the image to be segmented with prototypes acquired from a few annotated examples. A recent method called the ADNet, inspired by anomaly detection only computes one single prototype. This prototype captures the properties of the foreground segment. In this paper, the aim is to investigate whether the ADNet may benefit from more than one prototype to capture foreground properties. We take inspiration from the very recent idea of self-guidance, where an initial prediction of the support image is used to compute two new prototypes, representing the covered region and the missed region. We couple these more fine-grained prototypes with the ADNet framework to form what we refer to as the self-guided ADNet, or SG-ADNet for short. We evaluate the proposed SG-ADNet on a benchmark cardiac MRI data set, achieving competitive overall performance compared to the baseline ADNet, helping reduce over-segmentation errors for some classes

Evaluating (linked) metadata transformations across cultural heritage domains

This paper describes an approach to the evaluation of different aspects in the transformation of existing metadata into Linked data-compliant knowledge bases. At Oslo and Akershus University College of Applied Sciences, in the TORCH project, we are working on three different experimental case studies on extraction and mapping of broadcasting data and the interlinking of these with transformed library data. The case studies are investigating problems of heterogeneity and ambiguity in and between the domains, as well as problems arising in the interlinking process. The proposed approach makes it possible to collaborate on evaluation across different experiments, and to rationalize and streamline the process