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Alirezaie, M., Längkvist, M., Sioutis, M. & Loutfi, A. (2018). A Symbolic Approach for Explaining Errors in Image Classification Tasks. In: : . Paper presented at 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, July 13-19, 2018.
Open this publication in new window or tab >>A Symbolic Approach for Explaining Errors in Image Classification Tasks
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.

National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-68000 (URN)
Conference
27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, July 13-19, 2018
Note

IJCAI Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge

Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2018-07-26Bibliographically approved
Längkvist, M., Jendeberg, J., Thunberg, P., Loutfi, A. & Lidén, M. (2018). Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks. Computers in Biology and Medicine, 97, 153-160
Open this publication in new window or tab >>Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
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2018 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 97, p. 153-160Article in journal (Refereed) Published
Abstract [en]

Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Computer aided detection, Ureteral stone, Convolutional neural networks, Computed tomography, Training set selection, False positive reduction
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:oru:diva-67139 (URN)10.1016/j.compbiomed.2018.04.021 (DOI)000435623700015 ()29730498 (PubMedID)2-s2.0-85046800526 (Scopus ID)
Note

Funding Agencies:

Nyckelfonden  OLL-597511 

Vinnova under the project "Interactive Deep Learning for 3D image analysis"  

Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-08-30Bibliographically approved
Lidén, M., Jendeberg, J., Längkvist, M., Loutfi, A. & Thunberg, P. (2018). Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network: more than local features needed. In: : . Paper presented at European Congress of Radiology (ECR) 2018, Vienna, Austria, 28 Feb.-4 Mar., 2018.
Open this publication in new window or tab >>Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network: more than local features needed
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2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Purpose: To develop a deep learning method for assisting radiologists in the discrimination between distal ureteral stones and pelvic phleboliths in thin slice CT images, and to evaluate whether this differentiation is possible using only local features.

Methods and materials: A limited field-of-view image data bank was retrospectively created, consisting of 5x5x5 cm selections from 1 mm thick unenhanced CT images centered around 218 pelvis phleboliths and 267 distal ureteral stones in 336 patients. 50 stones and 50 phleboliths formed a validation cohort and the remainder a training cohort. Ground truth was established by a radiologist using the complete CT examination during inclusion.The limited field-of-view CT stacks were independently reviewed and classified as containing a distal ureteral stone or a phlebolith by seven radiologists. Each cropped stack consisted of 50 slices (5x5 cm field-of-view) and was displayed in a standard PACS reading environment. A convolutional neural network using three perpendicular images (2.5D-CNN) from the limited field-of-view CT stacks was trained for classification.

Results: The 2.5D-CNN obtained 89% accuracy (95% confidence interval 81%-94%) for the classification in the unseen validation cohort while the accuracy of radiologists reviewing the same cohort was 86% (range 76%-91%). There was no statistically significant difference between 2.5D-CNN and radiologists.

Conclusion: The 2.5D-CNN achieved radiologist level classification accuracy between distal ureteral stones and pelvic phleboliths when only using the local features. The mean accuracy of 86% for radiologists using limited field-of-view indicates that distant anatomical information that helps identifying the ureter’s course is needed.

National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:oru:diva-67372 (URN)
Conference
European Congress of Radiology (ECR) 2018, Vienna, Austria, 28 Feb.-4 Mar., 2018
Available from: 2018-06-20 Created: 2018-06-20 Last updated: 2018-06-20Bibliographically approved
Akalin, N., Kiselev, A., Kristoffersson, A. & Loutfi, A. (2018). Enhancing Social Human-Robot Interaction with Deep Reinforcement Learning.. In: Proc. FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction, 2018: . Paper presented at FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction (AI-MHRI), Stockholm, Sweden 14-15 July, 2018 (pp. 48-50). MHRI
Open this publication in new window or tab >>Enhancing Social Human-Robot Interaction with Deep Reinforcement Learning.
2018 (English)In: Proc. FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction, 2018, MHRI , 2018, p. 48-50Conference paper, Published paper (Refereed)
Abstract [en]

This research aims to develop an autonomous social robot for elderly individuals. The robot will learn from the interaction and change its behaviors in order to enhance the interaction and improve the user experience. For this purpose, we aim to use Deep Reinforcement Learning. The robot will observe the user’s verbal and nonverbal social cues by using its camera and microphone, the reward will be positive valence and engagement of the user.

Place, publisher, year, edition, pages
MHRI, 2018
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-68709 (URN)10.21437/AI-MHRI.2018-12 (DOI)
Conference
FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction (AI-MHRI), Stockholm, Sweden 14-15 July, 2018
Projects
SOCRATES
Available from: 2018-09-03 Created: 2018-09-03 Last updated: 2018-09-04Bibliographically approved
Köckemann, U., Alirezaie, M., Karlsson, L. & Loutfi, A. (2018). Integrating Ontologies for Context-based Constraint-based Planning. In: MRC 2018: Modelling and Reasoning in Context. Paper presented at Tenth InternationalWorkshop Modelling and Reasoning in Context (MRC), Stockholm, Sweden, July 13, 2018 (pp. 22-29).
Open this publication in new window or tab >>Integrating Ontologies for Context-based Constraint-based Planning
2018 (English)In: MRC 2018: Modelling and Reasoning in Context, 2018, p. 22-29Conference paper, Published paper (Refereed)
Abstract [en]

We describe an approach for integrating ontologies with a constraint-based planner to compile configuration planning domains based on the current context. We consider two alternative approaches: The first one integrates SPARQL queries directly with the planner while the second one generates SPARQL queries dynamically from provided triples. The first approach offers the full freedom of the SPARQL query language, while the second offers a more dynamic way for the planner to influence queries based on what is currently relevant for the planner. We evaluate the approach based on how much redundancy is removed by “outsourcing” knowledge into the ontology compared to modeling it directly into the domain of the planner.

National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-67849 (URN)
Conference
Tenth InternationalWorkshop Modelling and Reasoning in Context (MRC), Stockholm, Sweden, July 13, 2018
Available from: 2018-07-10 Created: 2018-07-10 Last updated: 2018-08-08Bibliographically approved
Alexopoulou, S., Fart, F., Jonsson, A.-S., Karni, L., Kenalemang, L. M., Krishna, S., . . . Widell, B. (2018). Successful ageing in an interdisciplinary context: popular science presentations. Örebro: Örebro University
Open this publication in new window or tab >>Successful ageing in an interdisciplinary context: popular science presentations
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2018 (English)Book (Other (popular science, discussion, etc.))
Place, publisher, year, edition, pages
Örebro: Örebro University, 2018. p. 127
National Category
Gerontology, specialising in Medical and Health Sciences Other Social Sciences not elsewhere specified
Identifiers
urn:nbn:se:oru:diva-66306 (URN)978-91-87789-18-2 (ISBN)
Available from: 2018-04-03 Created: 2018-04-03 Last updated: 2018-09-14Bibliographically approved
Akalin, N., Kiselev, A., Kristoffersson, A. & Loutfi, A. (2017). An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security. In: Kheddar, Abderrahmane; Yoshida, Eiichi; Ge, Shuzhi Sam; Suzuki, Kenji; Cabibihan, John-John; Eyssel, Friederike; He, Hongsheng (Ed.), Kheddar, A.; Yoshida, E.; Ge, S.S.; Suzuki, K.; Cabibihan, J-J:, Eyssel, F:, He, H. (Ed.), Social Robotics: 9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings. Paper presented at 9th International Conference on Social Robotics (ICSR 2017), Tsukuba, Japan, November 22-24, 2017 (pp. 628-637). Springer International Publishing
Open this publication in new window or tab >>An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
2017 (English)In: Social Robotics: 9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings / [ed] Kheddar, A.; Yoshida, E.; Ge, S.S.; Suzuki, K.; Cabibihan, J-J:, Eyssel, F:, He, H., Springer International Publishing , 2017, p. 628-637Conference paper, Published paper (Refereed)
Abstract [en]

The aim of the study presented in this paper is to develop a quantitative evaluation tool of the sense of safety and security for robots in eldercare. By investigating the literature on measurement of safety and security in human-robot interaction, we propose new evaluation tools. These tools are semantic differential scale questionnaires. In experimental validation, we used the Pepper robot, programmed in the way to exhibit social behaviors, and constructed four experimental conditions varying the degree of the robot’s non-verbal behaviors from no gestures at all to full head and hand movements. The experimental results suggest that both questionnaires (for the sense of safety and the sense of security) have good internal consistency.

Place, publisher, year, edition, pages
Springer International Publishing, 2017
Series
Lecture Notes in Computer Science ; 10652
Keywords
Sense of safety, Sense of security, Eldercare, Video-based evaluation, Quantitative evaluation tool
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-62768 (URN)10.1007/978-3-319-70022-9_62 (DOI)978-3-319-70022-9 (ISBN)978-3-319-70021-2 (ISBN)
Conference
9th International Conference on Social Robotics (ICSR 2017), Tsukuba, Japan, November 22-24, 2017
Projects
SOCRATES
Funder
EU, Horizon 2020, 721619
Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2017-11-30Bibliographically approved
Alirezaie, M., Renoux, J., Köckemann, U., Kristoffersson, A., Karlsson, L., Blomqvist, E., . . . Loutfi, A. (2017). An Ontology-based Context-aware System for Smart Homes: E-care@home. Sensors, 17(7), Article ID 1586.
Open this publication in new window or tab >>An Ontology-based Context-aware System for Smart Homes: E-care@home
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 7, article id 1586Article in journal, Editorial material (Refereed) Published
Abstract [en]

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

Place, publisher, year, edition, pages
Basel: MDPI AG, 2017
Keywords
ambient assisted living; context awareness; Internet of Things; ontologies; activity recognition; smart homes
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-58600 (URN)10.3390/s17071586 (DOI)000407517600125 ()2-s2.0-85021911117 (Scopus ID)
Projects
E-Care@Home
Funder
Knowledge Foundation
Available from: 2017-07-07 Created: 2017-07-07 Last updated: 2017-10-24Bibliographically approved
Alirezaie, M., Kiselev, A., Längkvist, M., Klügl, F. & Loutfi, A. (2017). An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring. Sensors, 17(11), Article ID 2545.
Open this publication in new window or tab >>An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 11, article id 2545Article in journal, Editorial material (Refereed) Published
Abstract [en]

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

Place, publisher, year, edition, pages
M D P I AG, 2017
Keywords
satellite imagery data; natural hazards; ontology; reasoning; path finding
National Category
Computer Systems
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-62134 (URN)10.3390/s17112545 (DOI)000416790500107 ()29113073 (PubMedID)2-s2.0-85033372857 (Scopus ID)
Projects
Semantic Robot
Available from: 2017-11-05 Created: 2017-11-05 Last updated: 2018-01-03Bibliographically approved
Renoux, J., Alirezaie, M., Karlsson, L., Köckemann, U., Pecora, F. & Loutfi, A. (2017). Context Recognition in Multiple Occupants Situations: Detecting the Number of Agents in a Smart Home Environment with Simple Sensors. In: Knowledge-based techniques for problem solving and reasoning(KnowProS 2017): A workshop at AAAI 2017, February 5, 2017, San Francisco, U.S.A.. Paper presented at Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning (KnowProS’17) (pp. 758-764). Palo Alto: AAAI Press, ws17, Article ID WS-17-12.
Open this publication in new window or tab >>Context Recognition in Multiple Occupants Situations: Detecting the Number of Agents in a Smart Home Environment with Simple Sensors
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2017 (English)In: Knowledge-based techniques for problem solving and reasoning(KnowProS 2017): A workshop at AAAI 2017, February 5, 2017, San Francisco, U.S.A., Palo Alto: AAAI Press, 2017, Vol. ws17, p. 758-764, article id WS-17-12Conference paper, Published paper (Refereed)
Abstract [en]

Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count the number of users in the environment is important in order to accurately recognize the activities of (groups of) agents. For smart environments without cameras, the problem of counting the number of agents is non-trivial. This is in part due to the difficulty of using a single non-vision based sensors to discriminate between one or several persons, and thus information from several sensors must be combined in order to reason about the presence of several agents. In this paper we address the problem of counting the number of agents in a topologically known environment using simple sensors that can indicate anonymous human presence. To do so, we connect an ontology to a probabilistic model (a Hidden Markov Model) in order to estimate the number of agents in each section of the environment. We evaluate our methods on a smart home setup where a number of motion and pressure sensors are distributed in various rooms of the home.

Place, publisher, year, edition, pages
Palo Alto: AAAI Press, 2017
Series
The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence: Technical Reports WS-17-01 - WS-17-15
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-62763 (URN)9781577357865 (ISBN)1577357868 (ISBN)
Conference
Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning (KnowProS’17)
Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2018-01-26Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3122-693X

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