Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance.

Background: Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive pre-training on medical images. However, further research is necessary to incorporate the particular characteristics of these images.

Validation of the ViGaTu Immersive Virtual Reality Endoscopy Training System for Physicians and Nurses.

Background And Aims: Endoscopy simulators are primarily designed to provide training in interventions performed during procedures. Peri-interventional tasks such as checking patient data, filling out forms for team time-out, patient monitoring, and performing sedation are often not covered. This study assesses the face, content, and construct validity of the ViGaTu (Virtual Gastro Tutor) immersive virtual reality (VR) simulator in teaching these skills.

Medical volume segmentation by overfitting sparsely annotated data.

Purpose: Semantic segmentation is one of the most significant tasks in medical image computing, whereby deep neural networks have shown great success. Unfortunately, supervised approaches are very data-intensive, and obtaining reliable annotations is time-consuming and expensive. Sparsely labeled approaches, such as bounding boxes, have shown some success in reducing the annotation time.

Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL).

The study's primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years.

Decoding binary decisions under differential target probabilities from pupil dilation: A random forest approach.

Although our pupils slightly dilate when we look at an intended target, they do not when we look at irrelevant distractors. This finding suggests that it may be possible to decode the intention of an observer, understood as the outcome of implicit covert binary decisions, from the pupillary dynamics over time. However, few previous works have investigated the feasibility of this approach and the few that did, did not control for possible confounds such as motor-execution, changes in brightness, or target and distractor probability.

Quality of Physical Activity Apps: Systematic Search in App Stores and Content Analysis.

Background: Physical inactivity is a major contributor to the development and persistence of chronic diseases. Mobile health apps that foster physical activity have the potential to assist in behavior change. However, the quality of the mobile health apps available in app stores is hard to assess for making informed decisions by end users and health care providers.

Improved automatic detection of herpesvirus secondary envelopment stages in electron microscopy by augmenting training data with synthetic labelled images generated by a generative adversarial network.

Detailed analysis of secondary envelopment of the herpesvirus human cytomegalovirus (HCMV) by transmission electron microscopy (TEM) is crucial for understanding the formation of infectious virions. Here, we present a convolutional neural network (CNN) that automatically recognises cytoplasmic capsids and distinguishes between three HCMV capsid envelopment stages in TEM images. 315 TEM images containing 2,610 expert-labelled capsids of the three classes were available for CNN training.

Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning.

Detecting crossovers in cryo-electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a sample. Currently, the crossover locations are picked by hand, which introduces errors and is a time-consuming procedure. With the rise of deep learning in computer vision tasks, the automation of such problems has become more and more applicable.

Detection of herpesvirus capsids in transmission electron microscopy images using transfer learning.

The detailed analysis of secondary envelopment of the Human betaherpesvirus 5/human cytomegalovirus (HCMV) from transmission electron microscopy (TEM) images is an important step towards understanding the mechanisms underlying the formation of infectious virions. As a step towards a software-based quantification of different stages of HCMV virion morphogenesis in TEM, we developed a transfer learning approach based on convolutional neural networks (CNNs) that automatically detects HCMV nucleocapsids in TEM images. In contrast to existing image analysis techniques that require time-consuming manual definition of structural features, our method automatically learns discriminative features from raw images without the need for extensive pre-processing.

Identifying Features of Bodily Expression As Indicators of Emotional Experience during Multimedia Learning.

The importance of emotions experienced by learners during their interaction with multimedia learning systems, such as serious games, underscores the need to identify sources of information that allow the recognition of learners' emotional experience without interrupting the learning process. Bodily expression is gaining in attention as one of these sources of information. However, to date, the question of how bodily expression can convey different emotions has largely been addressed in research relying on acted emotion displays.