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SHERPA has created rural Science-Society-Policy interfaces to engage citizens, stakeholders, policy-makers and scientists in the joint development of strategic thinking and practical recommendations for the formulation of modern rural policies. SHERPA captured and used results of ongoing and past research projects and created spaces and channels for meaningful interaction. SHERPA Multi-Actor Platforms (MAPs) were the main fora for co-learning and co-creation of knowledge between actors at European and regional levels. During the four-year project period, 40 regional MAPs and one EU-level MAP were set out to explore and test possibilities for improved policy stimulation and multi-actor and multi-level governance of rural development. The purpose was to tune local activities, policies and research to foster rural development policy by engaging citizens, rural stakeholders, researchers and policy makers. The assessment of existing knowledge to identify research gaps and the evaluation of the practical role and effectiveness of implemented policy in regional development was the basis for co-constructing recommendations for future rural development policies. A specific endeavour was put into inspiring and skill-building MAPs' capacities such that they continue after the end of the project.

Radboud University, 28 januari 2022, Promotores : Laak, J.A.W.M. van der, Ginneken, B. van Co-promotores : Litjens, G.J.S., Hulsbergen-van de Kaa, C.A., Contains fulltext : 241550.pdf (Publisher’s version ) (Open Access)

Radboud University, 28 januari 2022, Promotores : Laak, J.A.W.M. van der, Ginneken, B. van Co-promotores : Litjens, G.J.S., Hulsbergen-van de Kaa, C.A., Contains fulltext : 241550.pdf (Publisher’s version ) (Open Access)

Radboud University, 28 januari 2022, Promotores : Laak, J.A.W.M. van der, Ginneken, B. van Co-promotores : Litjens, G.J.S., Hulsbergen-van de Kaa, C.A., Contains fulltext : 241550.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 232872.pdf (Publisher’s version ) (Open Access), The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

Contains fulltext : 238871.pdf (Publisher’s version ) (Open Access), Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under- and overtreatment. Artificial intelligence (AI) systems have shown promise in assisting pathologists to perform Gleason grading, which could help address this problem. In this mini-review, we highlight studies reporting on the development of AI systems for cancer detection and Gleason grading, and discuss the progress needed for widespread clinical implementation, as well as anticipated future developments. PATIENT SUMMARY: This mini-review summarizes the evidence relating to the validation of artificial intelligence (AI)-assisted cancer detection and Gleason grading of prostate cancer in biopsies, and highlights the remaining steps required prior to its widespread clinical implementation. We found that, although there is strong evidence to show that AI is able to perform Gleason grading on par with experienced uropathologists, more work is needed to ensure the accuracy of results from AI systems in diverse settings across different patient populations, digitization platforms, and pathology laboratories.

Contains fulltext : 235135.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 232024.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 232872.pdf (Publisher’s version ) (Open Access), The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

Contains fulltext : 238871.pdf (Publisher’s version ) (Open Access), Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under- and overtreatment. Artificial intelligence (AI) systems have shown promise in assisting pathologists to perform Gleason grading, which could help address this problem. In this mini-review, we highlight studies reporting on the development of AI systems for cancer detection and Gleason grading, and discuss the progress needed for widespread clinical implementation, as well as anticipated future developments. PATIENT SUMMARY: This mini-review summarizes the evidence relating to the validation of artificial intelligence (AI)-assisted cancer detection and Gleason grading of prostate cancer in biopsies, and highlights the remaining steps required prior to its widespread clinical implementation. We found that, although there is strong evidence to show that AI is able to perform Gleason grading on par with experienced uropathologists, more work is needed to ensure the accuracy of results from AI systems in diverse settings across different patient populations, digitization platforms, and pathology laboratories.

Contains fulltext : 235135.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 232024.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 235135.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 232024.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 232872.pdf (Publisher’s version ) (Open Access), The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

Contains fulltext : 238871.pdf (Publisher’s version ) (Open Access), Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under- and overtreatment. Artificial intelligence (AI) systems have shown promise in assisting pathologists to perform Gleason grading, which could help address this problem. In this mini-review, we highlight studies reporting on the development of AI systems for cancer detection and Gleason grading, and discuss the progress needed for widespread clinical implementation, as well as anticipated future developments. PATIENT SUMMARY: This mini-review summarizes the evidence relating to the validation of artificial intelligence (AI)-assisted cancer detection and Gleason grading of prostate cancer in biopsies, and highlights the remaining steps required prior to its widespread clinical implementation. We found that, although there is strong evidence to show that AI is able to perform Gleason grading on par with experienced uropathologists, more work is needed to ensure the accuracy of results from AI systems in diverse settings across different patient populations, digitization platforms, and pathology laboratories.

The aim of this deliverable is to present the policy recommendations of the research project AgriLink. The goal of AgriLink is to stimulate transitions towards more sustainable European agricultures by i) furthering the understanding of the roles played by a wide range of advisory organisations and other advice providers in farmer decision-making regarding the adoption of innovation; and ii) enhancing the contribution to learning and innovation by testing methods to co-design improved services (with advisors, researchers, policy makers and farmers’ representatives).

Contains fulltext : 217309.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 217309.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 217309.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 212621.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 201132.pdf (publisher's version ) (Open Access)

Contains fulltext : 212621.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 201132.pdf (publisher's version ) (Open Access)

Contains fulltext : 212621.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 201132.pdf (publisher's version ) (Open Access)

2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI) (Berlin, Germany, 4-8 April 2016), Item does not contain fulltext, The indoor localisation problem is more complex than just finding whereabouts of users. Finding positions of users relative to the devices of a smart space is even more important. Unfortunately, configuring such systems manually is a tedious process, requires expert knowledge, and is sensitive to changes in the environment. Moreover, many existing solutions do not take user privacy into account. We propose a new system, called Simultaneous Localisation and Configuration (SLAC), to address the problem of locating devices and users relative to those devices, and combine this problem into a single estimation problem. The SLAC algorithm, based on FastSLAM, is able to locate devices using the received signal strength indicator (RSSI) of devices and motion data from users. Simulations have been used to show the performance in a controlled environment and the effect of the amount of RSSI updates on the localisation error. Live tests in non-trivial environments showed that we can achieve room level accuracy and that the localisation can be performed in real time. This is all done locally, i.e. running on a user's device, with respect for privacy and without using any prior information of the environment or device locations. Although promising, more work is required to increase accuracy in larger environments and to make the algorithm more robust for environment noise caused by walls and other objects. Existing techniques, e.g. map fusing, can alleviate these problems.

2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI) (Berlin, Germany, 4-8 April 2016), Item does not contain fulltext, The indoor localisation problem is more complex than just finding whereabouts of users. Finding positions of users relative to the devices of a smart space is even more important. Unfortunately, configuring such systems manually is a tedious process, requires expert knowledge, and is sensitive to changes in the environment. Moreover, many existing solutions do not take user privacy into account. We propose a new system, called Simultaneous Localisation and Configuration (SLAC), to address the problem of locating devices and users relative to those devices, and combine this problem into a single estimation problem. The SLAC algorithm, based on FastSLAM, is able to locate devices using the received signal strength indicator (RSSI) of devices and motion data from users. Simulations have been used to show the performance in a controlled environment and the effect of the amount of RSSI updates on the localisation error. Live tests in non-trivial environments showed that we can achieve room level accuracy and that the localisation can be performed in real time. This is all done locally, i.e. running on a user's device, with respect for privacy and without using any prior information of the environment or device locations. Although promising, more work is required to increase accuracy in larger environments and to make the algorithm more robust for environment noise caused by walls and other objects. Existing techniques, e.g. map fusing, can alleviate these problems.

2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI) (Berlin, Germany, 4-8 April 2016), Item does not contain fulltext, The indoor localisation problem is more complex than just finding whereabouts of users. Finding positions of users relative to the devices of a smart space is even more important. Unfortunately, configuring such systems manually is a tedious process, requires expert knowledge, and is sensitive to changes in the environment. Moreover, many existing solutions do not take user privacy into account. We propose a new system, called Simultaneous Localisation and Configuration (SLAC), to address the problem of locating devices and users relative to those devices, and combine this problem into a single estimation problem. The SLAC algorithm, based on FastSLAM, is able to locate devices using the received signal strength indicator (RSSI) of devices and motion data from users. Simulations have been used to show the performance in a controlled environment and the effect of the amount of RSSI updates on the localisation error. Live tests in non-trivial environments showed that we can achieve room level accuracy and that the localisation can be performed in real time. This is all done locally, i.e. running on a user's device, with respect for privacy and without using any prior information of the environment or device locations. Although promising, more work is required to increase accuracy in larger environments and to make the algorithm more robust for environment noise caused by walls and other objects. Existing techniques, e.g. map fusing, can alleviate these problems.

BNAIC 2012: The 24th Benelux Conference on Artificial Intelligence, Maastricht, The Netherlands, 25-26 October 2012, Item does not contain fulltext

BNAIC 2012: The 24th Benelux Conference on Artificial Intelligence, Maastricht, The Netherlands, 25-26 October 2012, Item does not contain fulltext

BNAIC 2012: The 24th Benelux Conference on Artificial Intelligence, Maastricht, The Netherlands, 25-26 October 2012, Item does not contain fulltext

Herbestemming van een voormalig gevangenisziekenhuis binnen een complexe stedenbouwkundige situatie. Kernpunten: herinrichten stedenbouwkundige context, nieuwe functie in gebouw, synergie oud en nieuw., RMIT, Architecture