SIFT and SURF based feature extraction for the anomaly detection

In this paper, we suggest a way, how to use SIFT and SURF algorithms to extract the image features for anomaly detection. We use those feature vectors to train various classifiers on a real-world dataset in the semi -supervised (with a small number of faulty samples) manner with a large number of classifiers and in the one-class (with no faulty samples) manner using the SVDD and SVM classifier. We prove, that the SIFT and SURF algorithms could be used as feature extractors, that they could be used to train a semi-supervised and one-class classifier with an accuracy around 89\% and that the performance of the one-class classifier could be comparable to the semi-supervised one. We also made our dataset and source code publicly available.Comment: 28th Conference STUDENT EEICT 2022, Brno University of Technolog

Machine Learning and Computer Vision Techniques in Bee Monitoring Applications

Machine learning and computer vision are dynamically growing fields, which have proven to be able to solve very complex tasks. They could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. In this paper, we present an overview of the state-of-the-art computer vision and machine learning applications used for bee monitoring. We also demonstrate the potential of those methods as an example of an automated bee counter algorithm. The paper is aimed at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce to them its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use the machine learning techniques for other applications in bee monitoring

Towards Phytoplankton Parasite Detection Using Autoencoders

Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their impact, we need improved detection methods to integrate phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated imaging devices usually produce high amount of phytoplankton image data, while the occurrence of anomalous phytoplankton data is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN based object detector. With this supervised approach and the model trained on plankton species and anomalies, we were able to reach the highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can detect also unknown anomalies and it does not require any annotated anomalous data that may not be always available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles, or air bubble detection, our paper is according to our best knowledge the first one which focuses on automated anomaly detection considering putative phytoplankton parasites or infections

T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers

Introduction: Lymphocyte infiltration (LI) is often seen in breast cancer but its importance remains controversial. A positive correlation of human epidermal growth factor receptor 2 (HER2) amplification and LI has been described, which was associated with a more favorable outcome. However, specific lymphocytes might also promote tumor progression by shifting the cytokine milieu in the tumor. Methods: Affymetrix HG-U133A microarray data of 1,781 primary breast cancer samples from 12 datasets were included. The correlation of immune system-related metagenes with different immune cells, clinical parameters, and survival was analyzed. Results: A large cluster of nearly 600 genes with functions in immune cells was consistently obtained in all datasets. Seven robust metagenes from this cluster can act as surrogate markers for the amount of different immune cell types in the breast cancer sample. An IgG metagene as a marker for B cells had no significant prognostic value. In contrast, a strong positive prognostic value for the T-cell surrogate marker (lymphocyte-specific kinase (LCK) metagene) was observed among all estrogen receptor (ER)-negative tumors and those ER-positive tumors with a HER2 overexpression. Moreover ER-negative tumors with high expression of both IgG and LCK metagenes seem to respond better to neoadjuvant chemotherapy. Conclusions: Precise definitions of the specific subtypes of immune cells in the tumor can be accomplished from microarray data. These surrogate markers define subgroups of tumors with different prognosis. Importantly, all known prognostic gene signatures uniformly assign poor prognosis to all ER-negative tumors. In contrast, the LCK metagene actually separates the ER-negative group into better or worse prognosis

Feature Space Reduction As Data Preprocessing For The Anomaly Detection

In this paper, we present two pipelines in order to reduce the feature space for anomalydetection using the One Class SVM. As a first stage of both pipelines, we compare the performanceof three convolutional autoencoders. We use the PCA method together with t-SNE as the first pipelineand the reconstruction errors based method as the second. Both methods have potential for theanomaly detection, but the reconstruction error metrics prove to be more robust for this task. Weshow that the convolutional autoencoder architecture doesn’t have a significant effect for this task andwe prove the potential of our approach on the real world dataset

Feature space reduction as data preprocessing for the anomaly detection

In this paper, we present two pipelines in order to reduce the feature space for anomaly detection using the One Class SVM. As a first stage of both pipelines, we compare the performance of three convolutional autoencoders. We use the PCA method together with t-SNE as the first pipeline and the reconstruction errors based method as the second. Both methods have potential for the anomaly detection, but the reconstruction error metrics prove to be more robust for this task. We show that the convolutional autoencoder architecture doesn't have a significant effect for this task and we prove the potential of our approach on the real world dataset.Comment: 27th Conference STUDENT EEICT 2020, Brno University of Technolog

Safety and Outcome of Revascularization Treatment in Patients With Acute Ischemic Stroke and COVID-19: The Global COVID-19 Stroke Registry.

BACKGROUND AND OBJECTIVES COVID-19 related inflammation, endothelial dysfunction and coagulopathy may increase the bleeding risk and lower efficacy of revascularization treatments in patients with acute ischemic stroke. We aimed to evaluate the safety and outcomes of revascularization treatments in patients with acute ischemic stroke and COVID-19. METHODS Retrospective multicenter cohort study of consecutive patients with acute ischemic stroke receiving intravenous thrombolysis (IVT) and/or endovascular treatment (EVT) between March 2020 and June 2021, tested for SARS-CoV-2 infection. With a doubly-robust model combining propensity score weighting and multivariate regression, we studied the association of COVID-19 with intracranial bleeding complications and clinical outcomes. Subgroup analyses were performed according to treatment groups (IVT-only and EVT). RESULTS Of a total of 15128 included patients from 105 centers, 853 (5.6%) were diagnosed with COVID-19. 5848 (38.7%) patients received IVT-only, and 9280 (61.3%) EVT (with or without IVT). Patients with COVID-19 had a higher rate of symptomatic intracerebral hemorrhage (SICH) (adjusted odds ratio [OR] 1.53; 95% CI 1.16-2.01), symptomatic subarachnoid hemorrhage (SSAH) (OR 1.80; 95% CI 1.20-2.69), SICH and/or SSAH combined (OR 1.56; 95% CI 1.23-1.99), 24-hour (OR 2.47; 95% CI 1.58-3.86) and 3-month mortality (OR 1.88; 95% CI 1.52-2.33).COVID-19 patients also had an unfavorable shift in the distribution of the modified Rankin score at 3 months (OR 1.42; 95% CI 1.26-1.60). DISCUSSION Patients with acute ischemic stroke and COVID-19 showed higher rates of intracranial bleeding complications and worse clinical outcomes after revascularization treatments than contemporaneous non-COVID-19 treated patients. Current available data does not allow direct conclusions to be drawn on the effectiveness of revascularization treatments in COVID-19 patients, or to establish different treatment recommendations in this subgroup of patients with ischemic stroke. Our findings can be taken into consideration for treatment decisions, patient monitoring and establishing prognosis

Safety and Outcome of Revascularization Treatment in Patients With Acute Ischemic Stroke and COVID-19: The Global COVID-19 Stroke Registry

BACKGROUND AND OBJECTIVES: COVID-19 related inflammation, endothelial dysfunction and coagulopathy may increase the bleeding risk and lower efficacy of revascularization treatments in patients with acute ischemic stroke. We aimed to evaluate the safety and outcomes of revascularization treatments in patients with acute ischemic stroke and COVID-19. METHODS: Retrospective multicenter cohort study of consecutive patients with acute ischemic stroke receiving intravenous thrombolysis (IVT) and/or endovascular treatment (EVT) between March 2020 and June 2021, tested for SARS-CoV-2 infection. With a doubly-robust model combining propensity score weighting and multivariate regression, we studied the association of COVID-19 with intracranial bleeding complications and clinical outcomes. Subgroup analyses were performed according to treatment groups (IVT-only and EVT). RESULTS: Of a total of 15128 included patients from 105 centers, 853 (5.6%) were diagnosed with COVID-19. 5848 (38.7%) patients received IVT-only, and 9280 (61.3%) EVT (with or without IVT). Patients with COVID-19 had a higher rate of symptomatic intracerebral hemorrhage (SICH) (adjusted odds ratio [OR] 1.53; 95% CI 1.16-2.01), symptomatic subarachnoid hemorrhage (SSAH) (OR 1.80; 95% CI 1.20-2.69), SICH and/or SSAH combined (OR 1.56; 95% CI 1.23-1.99), 24-hour (OR 2.47; 95% CI 1.58-3.86) and 3-month mortality (OR 1.88; 95% CI 1.52-2.33).COVID-19 patients also had an unfavorable shift in the distribution of the modified Rankin score at 3 months (OR 1.42; 95% CI 1.26-1.60). DISCUSSION: Patients with acute ischemic stroke and COVID-19 showed higher rates of intracranial bleeding complications and worse clinical outcomes after revascularization treatments than contemporaneous non-COVID-19 treated patients. Current available data does not allow direct conclusions to be drawn on the effectiveness of revascularization treatments in COVID-19 patients, or to establish different treatment recommendations in this subgroup of patients with ischemic stroke. Our findings can be taken into consideration for treatment decisions, patient monitoring and establishing prognosis