GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos

The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos

HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features

In this paper, we present HTAD: A Home Tasks Activities Dataset. The dataset contains wrist-accelerometer and audio data from people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities represent a subset of activities that are needed to be able to live independently. Being able to detect activities with wearable devices in real-time is important for the realization of assistive technologies with applications in different domains such as elderly care and mental health monitoring. Preliminary results show that using machine learning with the presented dataset leads to promising results, but also there is still improvement potential. By making this dataset public, researchers can test different machine learning algorithms for activity recognition, especially, sensor data fusion methodsacceptedVersio

Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure

The in vitro fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features

CELLULAR, A Cell Autophagy Imaging Dataset

Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, such as cancer, and hence, autophagy constitutes a potential therapeutic target. Image analysis to follow autophagy in cells, especially on high-content screens, has proven to be a bottleneck. Machine learning (ML) algorithms have recently emerged as crucial in analyzing images to efficiently extract information, thus contributing to a better understanding of the questions at hand. This paper presents CELLULAR, an open dataset consisting of images of cells expressing the autophagy reporter mRFP-EGFP-Atg8a with cell-specific segmentation masks. Each cell is annotated into either basal autophagy, activated autophagy, or unknown. Furthermore, we introduce some preliminary experiments using the dataset that can be used as a baseline for future research

Ear and hearing care programs for First Nations children: a scoping review

Background: Ear and hearing care programs are critical to early detection and management of otitis media (or middle ear disease). Otitis media and associated hearing loss disproportionately impacts First Nations children. This affects speech and language development, social and cognitive development and, in turn, education and life outcomes. This scoping review aimed to better understand how ear and hearing care programs for First Nations children in high-income colonial-settler countries aimed to reduce the burden of otitis media and increase equitable access to care. Specifically, the review aimed to chart program strategies, map the focus of each program against 4 parts of a care pathway (prevention, detection, diagnosis/management, rehabilitation), and to identify the factors that indicated the longer-term sustainability and success of programs. Method: A database search was conducted in March 2021 using Medline, Embase, Global Health, APA PsycInfo, CINAHL, Web of Science Core Collection, Scopus, and Academic Search Premier. Programs were eligible or inclusion if they had either been developed or run at any time between January 2010 to March 2021. Search terms encompassed terms such as First Nations children, ear and hearing care, and health programs, initiatives, campaigns, and services. Results: Twenty-seven articles met the criteria to be included in the review and described a total of twenty-one ear and hearing care programs. Programs employed strategies to: (i) connect patients to specialist services, (ii) improve cultural safety of services, and (iii) increase access to ear and hearing care services. However, program evaluation measures were limited to outputs or the evaluation of service-level outcome, rather than patient-based outcomes. Factors which contributed to program sustainability included funding and community involvement although these were limited in many cases. Conclusion: The result of this study highlighted that programs primarily operate at two points along the care pathway—detection and diagnosis/management, presumably where the greatest need lies. Targeted strategies were used to address these, some which were limited in their approach. The success of many programs are evaluated as outputs, and many programs rely on funding sources which can potentially limit longer-term sustainability. Finally, the involvement of First Nations people and communities typically only occurred during implementation rather than across the development of the program. Future programs should be embedded within a connected system of care and tied to existing policies and funding streams to ensure long term viability. Programs should be governed and evaluated by First Nations communities to further ensure programs are sustainable and are designed to meet community needs

Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis

Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of “black box” models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes

VISEM-Tracking, a human spermatozoa tracking dataset

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-assisted sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa

Njord: a fishing trawler dataset

Fish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and classification of fish from images or videos using machine learning or other analysis methods attracts growing attention. Surprisingly, little work has been done that considers what is happening on board the fishing vessels. On the deck of the boats, a lot of data and important information are generated with potential applications, such as automatic detection of accidents or automatic reporting of fish caught. This paper presents Njord, a fishing trawler dataset consisting of surveillance videos from a modern off-shore fishing trawler at sea. The main goal of this dataset is to show the potential and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several possible research questions this dataset could help answer

Anti-PAD4 autoantibodies in rheumatoid arthritis: levels in serum over time and impact on PAD4 activity as measured with a small synthetic substrate

Isoform 4 of the human peptidylarginine deiminase (hPAD4) enzyme may be responsible for the citrullination of antigens in rheumatoid arthritis (RA) and has been shown to be itself the target of disease-specific autoantibodies. Here, we have tested whether the level of serum anti-hPAD4 antibodies in RA patients is stable over a period of 10 years and whether the antibodies influence hPAD4-mediated deimination of the small substrate N-α-Benzoyl-l-arginine ethyl ester. RA sera (n = 128) obtained at baseline and after 10 years were assessed for anti-hPAD4 antibodies by a specific immunoassay. For 118 RA patients, serum anti-hPAD4 IgG levels were stable over 10 years. Seven patients who were negative for anti-PAD4 IgG at baseline had become positive after 10 years. Further, total IgG from selected RA patients and controls were purified, and a fraction was depleted for anti-hPAD4 antibodies. Kinetic deimination assays were performed with total IgG and depleted fractions. The kcat and Km values of hPAD4-mediated deimination of N-α-Benzoyl-l-arginine ethyl ester were not affected by the depletion of the anti-hPAD4 antibodies from the total IgG pool. In conclusion, RA patients remain positive for anti-hPAD4 antibodies over time and some patients who are initially anti-hPAD4 negative become positive later in the disease course. The anti-hPAD4 antibodies did not affect the enzymatic activity of hPAD4 when the small substrate N-α-Benzoyl-l-arginine ethyl ester was used. However, this finding may not exclude an effect of these autoantibodies on citrullination of protein substrates in RA