| 1. |
- Falkman, Göran, 1968-, et al.
(författare)
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SOMWeb - Towards an Infrastructure for Knowledge Sharing in Oral Medicine
- 2005
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Ingår i: Connecting Medical Informatics and Bio-Informatics: Proceedings of MIE2005 - The XIXth International Congress of the European Federation for Medical Informatics. - Amsterdam : IOS Press. - 1586035495 ; 116, s. 527-32, s. 527-532
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Konferensbidrag (refereegranskat)abstract
- In a net-based society, clinicians can come together for cooperative work and distance learning around a common medical material. This requires suitable techniques for cooperative knowledge management and user interfaces that are adapted to both the group as a whole and to individuals. To support distributed management and sharing of clinical knowledge, we propose the development of an intelligent web community for clinicians within oral medicine. This virtual meeting place will support the ongoing work on developing a digital knowledge base, providing a foundation for a more evidence-based oral medicine. The presented system is founded on the use and development of web services and standards for knowledge modelling and knowledge-based systems. The work is conducted within the frame of a well-established cooperation between oral medicine and computer science.
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| 2. |
- Ge, Chenjie, 1991, et al.
(författare)
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Co-Saliency-Enhanced Deep Recurrent Convolutional Networks for Human Fall Detection in E-Healthcare
- 2018
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. ; , s. 1572-1575
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the issue of fall detection from videos for e-healthcare and assisted-living. Instead of using conventional hand-crafted features from videos, we propose a fall detection scheme based on co-saliency-enhanced recurrent convolutional network (RCN) architecture for fall detection from videos. In the proposed scheme, a deep learning method RCN is realized by a set of Convolutional Neural Networks (CNNs) in segment-levels followed by a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), to handle the time-dependent video frames. The co-saliency-based method enhances salient human activity regions hence further improves the deep learning performance. The main contributions of the paper include: (a) propose a recurrent convolutional network (RCN) architecture that is dedicated to the tasks of human fall detection in videos; (b) integrate a co-saliency enhancement to the deep learning scheme for further improving the deep learning performance; (c) extensive empirical tests for performance analysis and evaluation under different network settings and data partitioning. Experiments using the proposed scheme were conducted on an open dataset containing multicamera videos from different view angles, results have shown very good performance (test accuracy 98.96%). Comparisons with two existing methods have provided further support to the proposed scheme.
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| 3. |
- Amundin, Mats, et al.
(författare)
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A proposal to use distributional models to analyse dolphin vocalisation
- 2017
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Ingår i: Proceedings of the 1st International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots, VIHAR 2017. - 9782956202905 ; , s. 31-32
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Konferensbidrag (refereegranskat)abstract
- This paper gives a brief introduction to the starting points of an experimental project to study dolphin communicative behaviour using distributional semantics, with methods implemented for the large scale study of human language.
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| 4. |
- Lindgren, Erik, 1980, et al.
(författare)
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Analysis of industrial X-ray computed tomography data with deep neural networks
- 2021
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Ingår i: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE. - 0277-786X .- 1996-756X. ; 11840
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Konferensbidrag (refereegranskat)abstract
- X-ray computed tomography (XCT) is increasingly utilized industrially at material- and process development as well as in non-destructive quality control; XCT is important to many emerging manufacturing technologies, for example metal additive manufacturing. These trends lead to increased needs of safe automatic or semi-automatic data interpretation, considered an open research question for many critical high value industrial products such as within the aerospace industry. By safe, we mean that the interpretation is not allowed to unawarely or unexpectedly fail; specifically the algorithms must react sensibly to inputs dissimilar to the training data, so called out-of-distribution (OOD) inputs. In this work we explore data interpretation with deep neural networks to address: robust safe data interpretation which includes a confidence estimate with respect to OOD data, an OOD detector; generation of realistic synthetic material aw indications for the material science and nondestructive evaluation community. We have focused on industrial XCT related challenges, addressing difficulties with spatially correlated X-ray quantum noise. Results are reported on training auto-encoders (AE) and generative adversarial networks (GAN), on a publicly available XCT dataset of additively manufactured metal. We demonstrate that adding modeled X-ray noise during training reduces artefacts in the generated imperfection indications as well as improves the OOD detector performance. In addition, we show that the OOD detector can detect real and synthetic OOD data and still model the accepted in-distribution data down to the X-ray noise levels.
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| 5. |
- Dravins, Christina, et al.
(författare)
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Exploring and enjoying non-speech sounds through a cochlear implant: the therapy of music
- 2010
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Ingår i: 11th International Conference on Cochlear Implants and other Implantable Technologies. - Karolinska University Hospital : Karolinska University Hospital. ; , s. 356-
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Konferensbidrag (refereegranskat)abstract
- Cochlear implant technology was initially designed to promote reception ofspeech sounds; however, music enjoyment remains a challenge. Music is aninfluential ingredient in our well-being, playing an important role in ourcognitive, physical and social development. For many cochlear implantrecipients it is not feasible to communicate how sounds are perceived, andconsequently the benefits of music listening may be reduced. Non-speechsounds may also be important to persons with multiple functional deficitsthat relay on information additional to verbatim for participating incommunication. Deaf-born children with multiple functional deficitsconstitute a special vulnerable group as lack of reaction to sound oftenis discouraging to caregivers. Individually adapted tools and methods forsound awareness may promote exploration and appreciation of theinformation mediated by the implant.Two current works involving habilitation through sound production andmusic will be discussed. First, the results from a pilot study aiming atfinding musical toys that can be adapted to help children explore theirhearing with engaging sounds and expressive interfaces will be presented.The findings indicate that children with multiple functional deficits canbe more inclined to use the auditory channel for communication and playthan the caregivers would anticipate.Second, the results of a recent questionnaire study, which compared themusic exposure and appreciation of preschool cochlear implant recipientswith their normally hearing peers will be presented. The data from thisstudy indicate that preschool children with cochlear implants spendroughly the same amount of time interacting with musical instruments athome and watching television programmes and DVDs which include music.However, the data indicate that these children receive less exposure torecorded music without visual stimuli and show less sophisticatedresponses to music. The provision and supported use of habilitationmaterials which encourage interaction with music might therefore bebeneficial.
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| 6. |
- Hedenberg, Klas, 1968-, et al.
(författare)
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Obstacle Detection For Thin Horizontal Structures
- 2008
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Ingår i: World Congress on Engineering and Computer Science. - Hong Kong : International Association of Engineers. - 9789889867102 ; , s. 689-693
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Konferensbidrag (refereegranskat)abstract
- Many vision-based approaches for obstacle detection often state that vertical thin structure is of importance, e.g. poles and trees. However, there are also problem in detecting thin horizontal structures. In an industrial case there are horizontal objects, e.g. cables and fork lifts, and slanting objects, e.g. ladders, that also has to be detected. This paper focuses on the problem to detect thin horizontal structures. The system uses three cameras, situated as a horizontal pair and a vertical pair, which makes it possible to also detect thin horizontal structures. A comparison between a sparse disparity map based on edges and a dense disparity map with a column and row filter is made. Both methods use the Sum of Absolute Difference to compute the disparity maps. Special interest has been in scenes with thin horizontal objects. Tests show that the sparse dense method based on the Canny edge detector works better for the environments we have tested.
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| 7. |
- Ge, Chenjie, 1991, et al.
(författare)
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3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images
- 2018
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479970612 ; , s. 141-145
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Konferensbidrag (refereegranskat)abstract
- This paper addresses issues of grading brain tumor, glioma, from Magnetic Resonance Images (MRIs). Although feature pyramid is shown to be useful to extract multi-scale features for object recognition, it is rarely explored in MRI images for glioma classification/grading. For glioma grading, existing deep learning methods often use convolutional neural networks (CNNs) to extract single-scale features without considering that the scales of brain tumor features vary depending on structure/shape, size, tissue smoothness, and locations. In this paper, we propose to incorporate the multi-scale feature learning into a deep convolutional network architecture, which extracts multi-scale semantic as well as fine features for glioma tumor grading. The main contributions of the paper are: (a) propose a novel 3D multi-scale convolutional network architecture for the dedicated task of glioma grading; (b) propose a novel feature fusion scheme that further refines multi-scale features generated from multi-scale convolutional layers; (c) propose a saliency-aware strategy to enhance tumor regions of MRIs. Experiments were conducted on an open dataset for classifying high/low grade gliomas. Performance on the test set using the proposed scheme has shown good results (with accuracy of 89.47%).
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| 8. |
- Bresin, Roberto, et al.
(författare)
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Auditory feedback through continuous control of crumpling sound synthesis
- 2008
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Ingår i: Proceedings of Sonic Interaction Design. - : IUAV University of Venice. - 9788890341304 ; , s. 23-28
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Konferensbidrag (refereegranskat)abstract
- A realtime model for the synthesis of crumpling sounds ispresented. By capturing the statistics of short sonic transients which give rise to crackling noise, it allows for a consistent description of a broad spectrum of audible physical processes which emerge in several everyday interaction contexts.The model drives a nonlinear impactor that sonifies every transient, and it can be parameterized depending on the physical attributes of the crumpling material. Three different scenarios are described, respectively simulating the foot interaction with aggregate ground materials, augmenting a dining scenario, and affecting the emotional content of a footstep sequence. Taken altogether, they emphasize the potential generalizability of the model to situations in which a precise control of auditory feedback can significantly increase the enactivity and ecological validity of an interface.
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| 9. |
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| 10. |
- Sodhro, Ali Hassan, 1986-, et al.
(författare)
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Internet of medical things for independent living and re-learning
- 2021
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Ingår i: GLOBAL HEALTH 2021 : The Tenth International Conference on Global Health Challenges At: Barcelona, Spain. - 9781612088921 ; , s. 1-5, s. 1-5
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This position paper gives better insight about the role and importance of Internet of Medical Things (IoMT) for independent living and re-learning for older adults. Sensing Technologies are the paradigm shift for transforming conventional healthcare practices into the smart, and self-assisted activities, which are envisioned for today's medical world. Internet of Things (IoT) and IoMT are the interrelated technologies for promoting independent living and re-learning practices. In this paper, re-learning is defined as the process for adults to recover useful instrumental activities of daily living skills that have been lost after an impairment.
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