Effect of domain knowledge encoding in CNN model architecture—a prostate cancer study using mpMRI images

Piotr Sobecki, Rafał Jóźwiak, Katarzyna Sklinda, Artur Przelaskowski

2021 Peer J, T. 9, e11006, s. 1-17

Background. Prostate cancer is one of the most common cancers worldwide. Currently, convolution neural networks (CNNs) are achieving remarkable success in various computer vision tasks, and in medical imaging research. Various CNN architectures and methodologies have been applied in the field of prostate cancer diagnosis. In this work, we evaluate the impact of the adaptation of a state-of-theart CNN architecture on domain knowledge related to problems in the diagnosis of prostate cancer. The architecture of the final CNN model was optimised on the basis of the Prostate Imaging Reporting and Data System (PI-RADS) standard, which is currently the best available indicator in the acquisition, interpretation, and reporting
of prostate multi-parametric magnetic resonance imaging (mpMRI) examinations.
Methods. A dataset containing 330 suspicious findings identified using mpMRI was used. Two CNN models were subjected to comparative analysis. Both implement the concept of decision-level fusion for mpMRI data, providing a separate network for each multi-parametric series. The first model implements a simple fusion of multiparametric features to formulate the final decision. The architecture of the second model reflects the diagnostic pathway of PI-RADS methodology, using information about a lesion’s primary anatomic location within the prostate gland. Both networks were experimentally tuned to successfully classify prostate cancer changes.
Results. The optimised knowledge-encoded model achieved slightly better classification results compared with the traditional model architecture (AUC = 0.84 vs. AUC = 0.82). We found the proposed model to achieve convergence significantly faster. Conclusions. The final knowledge-encoded CNN model provided more stable learning performance and faster convergence to optimal diagnostic accuracy. The results fail to demonstrate that PI-RADS-based modelling of CNN architecture can significantly improve performance of prostate cancer recognition using mpMRI.


Substance Use Disorder Status Moderates the Association between Personality Traits and Problematic Mobile Phone/Internet Use

Marta Demkow-Jania, Maciej Kopera, Elisa M. Trucco, Paweł Kobyliński, Anna Klimkiewicz, Małgorzata Abramowska, Anna Mach, Andrzej Jakubczyk

2021 Journal of Clinical Medicine, T. 10, z. 5, Art. no 919.

Background: Associations between personality traits and problematic smartphone use (PSU) among individuals with substance use disorder (SUD) have not been widely investigated. The current study aims to assess whether SUD status moderates the association between personality traits and PSU. Methods: The study group included 151 individuals with SUD and a normative sample (NS) comprised of 554 non-SUD students. The following self-report questionnaires were used: the Mobile Phone Problem Use Scale (MPPUS-10) to assess problematic smartphone use (PSU), the Internet Addiction Test (IAT) to assess intensity of internet use, and the NEO Five-Factor Inventory (NEO-FFI) to assess Personality traits. Results: SUD status moderated the association between neuroticism and openness to new experiences on PSU. That is, greater neuroticism and openness were significantly associated with more excessive PSU among the NS. In the SUD group, greater openness was a significant protective factor against PSU. Moderation results were similar when using the IAT (which was significantly correlated with MPPUS) as an outcome. Conclusions: The presence of SUD may influence how personality traits are associated with problematic mobile phone/internet use. Given that this is among one of the first studies examining this topic, findings should be replicated with additional studies.


The synergistic effect between interoceptive accuracy and alcohol use disorder status on pain sensitivity

Andrzej Jakubczyk, Paweł Wiśniewski, Elisa M. Trucco, Paweł Kobyliński, Justyna Zaorska, Jakub Skrzeszewski, Hubert Suszek, Marcin Wojnar, Maciej Kopera

2021 Addictive Behaviors, T. 112, Art. 106607, s. 1-7

Background: Interoceptive accuracy and pain sensitivity are both risk factors in the development of alcohol use disorder (AUD). However, the synergistic association between these two factors has not been investigated in an AUD sample. Therefore, the aim of the current study was to investigate whether the association between interoceptive accuracy and sensitivity to pain differed across AUD status.
Methods: The study group included 165 individuals diagnosed with AUD (88.1% men) and 110 healthy controls (HCs; 74.5% men). Interoceptive accuracy was assessed with the Schandry Task. The Pain Sensitivity Questionnaire was utilized to measure sensitivity to pain. Anxiety, biological sex, and age were included as covariates in a model examining the role of AUD status as a moderator in the association between interoceptive accuracy and pain sensitivity.
Results: A significant interaction was found between interoceptive accuracy and AUD status (b = −4.580, 95% CI = [−8.137, −1.022], p = 0.012, ΔR2 = 0.032). Findings indicate that interoceptive accuracy was negatively associated with pain sensitivity among individuals with AUD, while there was a trend for an opposite association among healthy controls.
Conclusion: We hypothesize that persistent alcohol drinking may contribute to disruption of the normative association between interoception and pain. Future studies should be conducted to develop knowledge on this association and to investigate its possible therapeutic significance and implications.


Two-Sample Median Test for Interval-Valued Data

Przemysław Grzegorzewski, Martyna Śpiewak

2021 W.: Uncertainty and Imprecision in Decision Making and Decision Support: New Challenges, Solutions and Perspectives : Selected Papers from BOS-2018, held on September 24-26, 2018, and IWIFSGN-2018, held on September 27-28, 2018 in Warsaw, Poland / Krassimir T. Atanassov, Vassia Atanassova, Janusz Kacprzyk, Andrzej Kaluszko, Maciej Krawczak, Jan W. Owsinski, Sotir Sotirov, Evdokia Sotirova, Eulalia Szmidt, Slawomir Zadrozny. Cham : Springer, s. 113-128.

The median two-sample test for the location problem is considered. We adopt this nonparametric test to interval-valued data perceived from the epistemic perspective, where the available observations are just interval-valued perceptions of the unknown true outcomes of the experiment. Unlike typical generalizations of statistical procedures into the interval-valued framework, the proposed test entails very low computational costs. However, the presence of interval-valued data results in set-valued p-value which leads no longer to a definite binary decision (reject or not reject the null hypothesis) but may indicate the abstention from making a final decision if the information is too vague.


3D or not 3D? Evaluation of the effectiveness of 3D-enhanced warning messages for communication in crisis situations

Grzegorz Banerski, Katarzyna Abramczuk, Cezary Biele

2020 SAFETY SCIENCE, T. 132, s. 104961

A common problem in crisis situations is the unwillingness of the local communities to undertake individual safety measures. This study examined whether the motivation of the local population can be increased if 3D animation are used for visualization of the potential disaster scenarios. Furthermore, the study aimed at finding out how the use of such material affects the ability of the audience to remember what preventive actions should be taken.
As part of the experiment, three versions of a video message about flood threat were prepared: a video limited to the presenter with no additional elements in the background, a classic TV-like video message format, and a video featuring 3D visualisations of the consequences of flooding. The videos were presented to groups of subjects who live in flood-prone areas. Protection Motivation Theory was used as the experiment’s framework.
We found that the type of warning message did not influence the level of acquired knowledge of protective actions. Important differences were found in the structure of the determinants of self-protective motivation across the groups. The group that watched the 3D enhanced video was the only one in which the more emotional cognitive path of threat appraisal had a significant impact on the level of motivation. As regards the level of motivation to take protective actions, the observed effects were more complex than expected. The paper discusses the potential impact of the results on efficient risk communication design and the usefulness of the use of 3D animation in warning messages.


A Bidirectional Iterative Algorithm for Nested Named Entity Recognition

Sławomir Dadas, Jarosław Protasiewicz

2020 IEEE Access, T. 8, s. 135091 - 135102

Nested named entity recognition (NER) is a special case of structured prediction in which annotated sequences can be contained inside each other. It is a challenging and significant problem in natural language processing. In this paper, we propose a novel framework for nested named entity recognition tasks. Our approach is based on a deep learning model which can be called in an iterative way, expanding the set of predicted entity mentions with each subsequent iteration. The proposed framework combines two such models trained to identify named entities in different directions: from general to specific ( outside-in ), and from specific to general ( inside-out ). The predictions of both models are then aggregated by a selection policy. We propose and evaluate several selection policies which can be used with our algorithm. Our method does not impose any restrictions on the length of entity mentions, number of entity classes, depth, or structure of the predicted output. The framework has been validated experimentally on four well-known nested named entity recognition datasets: GENIA, NNE, PolEval, and GermEval. The datasets differ in terms of domain (biomedical, news, mixed), language (English, Polish, German), and the structure of nesting (simple, complex). Through extensive tests, we prove that the approach we have proposed outperforms existing methods for nested named entity recognition.


A Neural Network Toolbox for Electricity Consumption Forecasting

Jarosław Protasiewicz

2020 W: Prediction of Customer Status in Corporate Banking Using Neural Networks / Stanisław Osowski, Łukasz Siereński ; Piscataway: IEEE, s. 1-6

Prediction of Customer Status in Corporate Banking Using Neural Networks. Glasgow, 2020-07-19 - 2020-07-24

The aim of this study is to propose a neural toolbox for electricity consumption prediction. The toolkit covers the implementation of three artificial neural networks, namely: (i) a multi-layered perceptron network; (ii) a radial basis function network; and (iii) a self-organising map. Moreover, the toolbox includes tools known as metamodels, which allow easy use of these networks for forecasting. There are two prediction systems, namely: (i) serially connected models; and (ii) a two-levelled structure containing a classifier and a set of parallel models. They have been validated experimentally in the task of electricity consumption prediction. The results and flexibility of use suggest that the neural toolbox should help users to develop prediction systems of electricity consumption more conveniently, as it is designed for that particular purpose. The toolbox has been developed as open source – no commercial software is required to use it.


Academic entrepreneurship and the research productivity in Poland

Marta Bojko, Anna Knapińska, Aldona Tomczyńska

2020 Industry and Innovation, APR 2020, 1-21

Is it possible to reconcile academic entrepreneurship with the internationalisation of scientists’ productivity? We provide an answer to this question through the statistical analysis of a representative survey of 811 scientists in Poland. Based on Robert K. Merton’s distinction between two types of scholars: local (oriented towards the national science system) and cosmopolitan (aiming at international achievements), we found that research productivity positively influences academic entrepreneurship, but that scientists who are productive locally engage in academic entrepreneurship more often than the cosmopolitan ones. This suggests that the internationalisation of one’s scientific activity, at least in countries transitioning from a local to a cosmopolitan mode of research, is an absorbing endeavour that only the most productive researchers can reconcile with academic entrepreneurship. Such countries should balance their policies regarding the career development of their scientists also to include the promotion of science-business cooperation and not just the internationalisation of research.


Annobot: Platform for Annotating and Creating Datasets through Conversation with a Chatbot

Rafał Poświata, Michał Perełkiewicz

2020 W: Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations / Michal Ptaszynski, Bartosz Ziolko; Barcelona : International Committee on Computational Linguistics (ICCL), 2020, s. 75-79

The 28th International Conference on Computational Linguistics, Barcelona, 2020-12-08 - 2020-12-13

In this paper, we introduce Annobot: a platform for annotating and creating datasets through conversation with a chatbot. This natural form of interaction has allowed us to create a more accessible and flexible interface, especially for mobile devices. Our solution has a wide range of applications such as data labelling for binary, multi-class/label classification tasks, preparing data for regression problems, or creating sets for issues such as machine translation, question answering or text summarization. Additional features include pre-annotation, active sampling, online learning and real-time inter-annotator agreement. The system is integrated with the popular messaging platform: Facebook Messanger. Usability experiment showed the advantages of the proposed platform compared to other labelling tools. The source code of Annobot is available under the GNU LGPL license at https://github.com/rafalposwiata/annobot.


Biologically Plausible Learning of Text Representation with Spiking Neural Networks

Marcin Białas, Marcin Mirończuk, Jacek Mańdziuk

2020 W: Parallel Problem Solving from Nature – PPSN XVI / Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, Heike Trautmann; Cham: Springer, s. 433-447

International Conference on Parallel Problem Solving from Nature. Leiden, 2020-09-05 - 2020-09-09

This study proposes a novel biologically plausible mechanism for generating low-dimensional spike-based text representation. First, we demonstrate how to transform documents into series of spikes (spike trains) which are subsequently used as input in the training process of a spiking neural network (SNN). The network is composed of biologically plausible elements, and trained according to the unsupervised Hebbian learning rule, Spike-Timing-Dependent Plasticity (STDP). After training, the SNN can be used to generate low-dimensional spike-based text representation suitable for text/document classification. Empirical results demonstrate that the generated text representation may be effectively used in text classification leading to an accuracy of 80.19% on the bydate version of the 20 newsgroups data set, which is a leading result amongst approaches that rely on low-dimensional text representations.