Improving transparency and representational generalizability through parallel continual learning.

This paper takes a parallel learning approach in continual learning scenarios. We define parallel continual learning as learning a sequence of tasks where the data for the previous tasks, whose distribution may have shifted over time, are also available while learning new tasks. We propose a parallel continual learning method by assigning subnetworks to each task, and simultaneously training only the assigned subnetworks on their corresponding tasks.

Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis.

Atopic dermatitis (AD) is a skin inflammatory disease affecting 10% of the population worldwide. Raster-scanning optoacoustic mesoscopy (RSOM) has recently shown promise in dermatological imaging. We conducted a comprehensive analysis using three machine-learning models, random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) for classifying healthy versus AD conditions, and sub-classifying different AD severities using RSOM images and clinical information.

Regional registration of whole slide image stacks containing major histological artifacts.

Background: High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack.

Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy.

Background: We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI.

Methods: A total of 14 T4NxM0 NPC patients with histologically proven "in field" recurrence in the post nasal space following curative intent IMRT were included in this study.

Improved tagged cardiac MRI myocardium strain analysis by leveraging cine segmentation.

Background And Objectives: Tagged MR images provide an effective way for regional analysis of the myocardium strain. A reliable myocardium strain analysis requires both correct segmentation and accurate motion tracking of the myocardium during the cardiac cycle. While many algorithms have been proposed for accurate tracking of the myocardium in tagged MR images, little focus has been placed on ensuring correct segmentation of the tagged myocardium during the cardiac cycle.

Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults.

Introduction: Accelerometers are commonly used to assess physical activity. Consumer activity trackers have become increasingly popular today, such as the Fitbit. This study aimed to compare the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn ActiGraph (wGT3X-BT) in free-living conditions.

Automatic basal slice detection for cardiac analysis.

Identification of the basal slice in cardiac imaging is a key step to measuring the ejection fraction of the left ventricle. Despite all the effort placed on automatic cardiac segmentation, basal slice identification is routinely performed manually. Manual identification, however, suffers from high interobserver variability.