Presentation by Prof. Dr. Henning Müller.
Overview:
- Medical image retrieval projects
- Image analysis and 3D texture modeling
- Data science evaluation infrastructures (ImageCLEF, VISCERAL, EaaS – Evaluation as a Service)
- What comes next?
On April 11th 2016, Prof. Prof. Henning Müller (HES-SO Valais-Wallis and Martinos Center) presented Challenges in medical imaging and the VISCERAL model at National Cancer Institute in Washington.
On March 23, 2016, Prof. Henning Müller (HES-SO Valais-Wallis and Martinos Center) presented Medical image analysis and big data evaluation infrastructures at Stanford medicine.
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
Themes and objectives:
To position FAIR as a key enabler to automate and accelerate R&D process workflows
FAIR Implementation within the context of a use case
Grounded in precise outcomes (e.g. faster and bigger science / more reuse of data to enhance value / increased ability to share data for collaboration and partnership)
To make data actionable through FAIR interoperability
Speakers:
Mathew Woodwark,Head of Data Infrastructure and Tools, Data Science & AI, AstraZeneca
Erik Schultes, International Science Coordinator, GO-FAIR
Georges Heiter, Founder & CEO, Databiology
On April 11th 2016, Prof. Prof. Henning Müller (HES-SO Valais-Wallis and Martinos Center) presented Challenges in medical imaging and the VISCERAL model at National Cancer Institute in Washington.
On March 23, 2016, Prof. Henning Müller (HES-SO Valais-Wallis and Martinos Center) presented Medical image analysis and big data evaluation infrastructures at Stanford medicine.
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
Themes and objectives:
To position FAIR as a key enabler to automate and accelerate R&D process workflows
FAIR Implementation within the context of a use case
Grounded in precise outcomes (e.g. faster and bigger science / more reuse of data to enhance value / increased ability to share data for collaboration and partnership)
To make data actionable through FAIR interoperability
Speakers:
Mathew Woodwark,Head of Data Infrastructure and Tools, Data Science & AI, AstraZeneca
Erik Schultes, International Science Coordinator, GO-FAIR
Georges Heiter, Founder & CEO, Databiology
Digital Pathology at John Hopkins
Practical Research and Clinical Considerations
Alexander Baras
Presented at the Digital Pathology Congress: USA. For more information visit: www.global-engage.com.
Federated Learning (FL) is a learning paradigm that enables collaborative learning without centralizing datasets. In this webinar, NVIDIA present the concept of FL and discuss how it can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare. Following the presentation, the panel debate on other elements that could drive the adoption of digital approaches more widely and help answer currently intractable science and business questions.
Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
Digital pathology and its importance as an omics data layerYves Sucaet
Bioinformatics and pathology are obvious scientific partners. Bioinformatics often takes places at the most basic (almost chemical, or even physical) level of life, but much of its procedures to obtain data are destructive. Pathology on the other hand takes place at a much more coarse level of data acquisition (usually where the physical properties of visible light end), but has the advantage of being rooted in the tradition of medicine. The traditional paradigm of pathology is "tissue is the issue". Morphology (exactly the component that often gets overlooked in bioinformatics) plays a large role and helps millions of patients each year around the world. Pathology is proven technology, bioinformatics is limited to niche applications.
With the development of whole slide imaging technology some twenty years ago, digital pathology became possible. Observations that used to be for the eyes of the pathologist only, could now be captured and translated into high-resolution pixels, and studied by and communicated to many. Many began to dream of automated tissue evaluation systems and AI-pathology, some even going as far as to suggest the replacement of the pathologist by intelligent computer systems.
Meanwhile in several areas of bioinformatics, new limits are being hit. Yes, we can do high-throughput experiments, but noisy datasets are often the results, (inter- and even intra-observer) replicability is difficult, and statistics only offer limited relief.
The goal of this introductory lecture is to highlight the problems as well as opportunities for both fields of study, and how exchange of experiences, and (in a later stadium) integration of techniques close the scientific gap that still exists in a great many areas.
There is no lack of pathology-centric workshops that offer insights into the world of algorithms. With the CPW event however, we take another approach. We want to bring together the most advanced groups in digital pathology, with the bioinformatics community, to explore the opportunities that exist on both sides of the fence.
We start by explaining the basic data types that are introduced by digital pathology. We also explain where they come from, and why this presents unique challenges when it comes to data mining and image analysis. Finally, we introduce PMA.start, a free software environment that can be used to universally gain access to digital pathology (imaging) data.
Bioinformatics groups can help quantify, model, and reduce morphological whole tissue data. Pathologists can help interpret and explain heterogeneous high-throughput datasets. And the first seeds of such collaboration can be planted right here, in Athens.
In this talk I'll discuss work in biomedical image and volume segmentation and classification, as well as outcome prediction modeling from insurance claims data that I've pursued at LifeOmic here in the Triangle. In the former case datasets include radiological image volumes, retinal fundus images, and cell images created with fluorescent microscopy. The latter includes MIMIC-III data represented as FHIR objects. I'll discuss the relative challenges and advantages of doing ML locally vs. on a cloud-based platform.
Functional and Architectural Requirements for Metadata: Supporting Discovery...Jian Qin
The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.
Curation and Preservation of Crystallography DataManjulaPatel
A presentation given by Manjula Patel (UKOLN) at "Chemistry in the Digital Age: A Workshop connecting research and education", June 11-12th 2009, Penn State University,
http://www.chem.psu.edu/cyberworkshop09
IDCC Workshop: Analysing DMPs to inform research data services: lessons from ...Amanda Whitmire
A workshop as part of the International Digital Curation Conference 2016 on DMP development and support. This presentation demonstrates how we can use data management plans as a source of information to better understand researcher data stewardship practices and how to support them. Be sure to see the slide notes to better understand the presentation (most slides are just photos/icons).
A very short, very minimal presentation I prepared for the Yale Libraries' SCOPA event to introduce librarians in diverse disciplines to the concepts and challenges of data curation.
Introduction to research data management; Lecture 01 for GRAD521Amanda Whitmire
Lesson 1: Introduction to research data management. From a series of lectures from a 10-week, 2-credit graduate-level course in research data management (GRAD521, offered at Oregon State University).
The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines."
Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation.
See also, "Whitmire, Amanda (2014): GRAD 521 Research Data Management Lectures. figshare. http://dx.doi.org/10.6084/m9.figshare.1003835. Retrieved 23:25, Jan 07, 2015 (GMT)"
At the Knime Berlin summit 2016, Prof. Dr. Dominique Genoud presented a novel way to implement a KNIME workflow that perform machine learning and signal processing on an Android platform. The use case was to detect soft falls (not from a standing position) using an Android watch. This application has a big impact on how we can detect automatically when elderly people fall from their bed of their chair. This work was originally based on the Master Thesis in Business Administration realized by Vincent Cuendet in 2015 at the HES-SO with the help of the FST (Fédération Suisse pour les Téléthèses), an organization that helps disabled and elderly people to keep their autonomy.
We propose a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter-variations of the feature space dimensions. The obtained Riesz-covariance descriptors lie on a manifold governed by Riemannian geometry requiring specific geodesic metrics to locally approximate scalar products. The latter are used to construct a kernel for support vector machines (SVM). The effectiveness of the presented models is evaluated on a dataset of 92 patients with non-small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy above 80, and highlighted the importance of covariance-based texture aggregation. At the end of the talk, computer tools will be presented to easily extract 3D radiomics quantitative features from PET-CT images.
Digital Pathology at John Hopkins
Practical Research and Clinical Considerations
Alexander Baras
Presented at the Digital Pathology Congress: USA. For more information visit: www.global-engage.com.
Federated Learning (FL) is a learning paradigm that enables collaborative learning without centralizing datasets. In this webinar, NVIDIA present the concept of FL and discuss how it can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare. Following the presentation, the panel debate on other elements that could drive the adoption of digital approaches more widely and help answer currently intractable science and business questions.
Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
Digital pathology and its importance as an omics data layerYves Sucaet
Bioinformatics and pathology are obvious scientific partners. Bioinformatics often takes places at the most basic (almost chemical, or even physical) level of life, but much of its procedures to obtain data are destructive. Pathology on the other hand takes place at a much more coarse level of data acquisition (usually where the physical properties of visible light end), but has the advantage of being rooted in the tradition of medicine. The traditional paradigm of pathology is "tissue is the issue". Morphology (exactly the component that often gets overlooked in bioinformatics) plays a large role and helps millions of patients each year around the world. Pathology is proven technology, bioinformatics is limited to niche applications.
With the development of whole slide imaging technology some twenty years ago, digital pathology became possible. Observations that used to be for the eyes of the pathologist only, could now be captured and translated into high-resolution pixels, and studied by and communicated to many. Many began to dream of automated tissue evaluation systems and AI-pathology, some even going as far as to suggest the replacement of the pathologist by intelligent computer systems.
Meanwhile in several areas of bioinformatics, new limits are being hit. Yes, we can do high-throughput experiments, but noisy datasets are often the results, (inter- and even intra-observer) replicability is difficult, and statistics only offer limited relief.
The goal of this introductory lecture is to highlight the problems as well as opportunities for both fields of study, and how exchange of experiences, and (in a later stadium) integration of techniques close the scientific gap that still exists in a great many areas.
There is no lack of pathology-centric workshops that offer insights into the world of algorithms. With the CPW event however, we take another approach. We want to bring together the most advanced groups in digital pathology, with the bioinformatics community, to explore the opportunities that exist on both sides of the fence.
We start by explaining the basic data types that are introduced by digital pathology. We also explain where they come from, and why this presents unique challenges when it comes to data mining and image analysis. Finally, we introduce PMA.start, a free software environment that can be used to universally gain access to digital pathology (imaging) data.
Bioinformatics groups can help quantify, model, and reduce morphological whole tissue data. Pathologists can help interpret and explain heterogeneous high-throughput datasets. And the first seeds of such collaboration can be planted right here, in Athens.
In this talk I'll discuss work in biomedical image and volume segmentation and classification, as well as outcome prediction modeling from insurance claims data that I've pursued at LifeOmic here in the Triangle. In the former case datasets include radiological image volumes, retinal fundus images, and cell images created with fluorescent microscopy. The latter includes MIMIC-III data represented as FHIR objects. I'll discuss the relative challenges and advantages of doing ML locally vs. on a cloud-based platform.
Functional and Architectural Requirements for Metadata: Supporting Discovery...Jian Qin
The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.
Curation and Preservation of Crystallography DataManjulaPatel
A presentation given by Manjula Patel (UKOLN) at "Chemistry in the Digital Age: A Workshop connecting research and education", June 11-12th 2009, Penn State University,
http://www.chem.psu.edu/cyberworkshop09
IDCC Workshop: Analysing DMPs to inform research data services: lessons from ...Amanda Whitmire
A workshop as part of the International Digital Curation Conference 2016 on DMP development and support. This presentation demonstrates how we can use data management plans as a source of information to better understand researcher data stewardship practices and how to support them. Be sure to see the slide notes to better understand the presentation (most slides are just photos/icons).
A very short, very minimal presentation I prepared for the Yale Libraries' SCOPA event to introduce librarians in diverse disciplines to the concepts and challenges of data curation.
Introduction to research data management; Lecture 01 for GRAD521Amanda Whitmire
Lesson 1: Introduction to research data management. From a series of lectures from a 10-week, 2-credit graduate-level course in research data management (GRAD521, offered at Oregon State University).
The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines."
Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation.
See also, "Whitmire, Amanda (2014): GRAD 521 Research Data Management Lectures. figshare. http://dx.doi.org/10.6084/m9.figshare.1003835. Retrieved 23:25, Jan 07, 2015 (GMT)"
At the Knime Berlin summit 2016, Prof. Dr. Dominique Genoud presented a novel way to implement a KNIME workflow that perform machine learning and signal processing on an Android platform. The use case was to detect soft falls (not from a standing position) using an Android watch. This application has a big impact on how we can detect automatically when elderly people fall from their bed of their chair. This work was originally based on the Master Thesis in Business Administration realized by Vincent Cuendet in 2015 at the HES-SO with the help of the FST (Fédération Suisse pour les Téléthèses), an organization that helps disabled and elderly people to keep their autonomy.
We propose a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter-variations of the feature space dimensions. The obtained Riesz-covariance descriptors lie on a manifold governed by Riemannian geometry requiring specific geodesic metrics to locally approximate scalar products. The latter are used to construct a kernel for support vector machines (SVM). The effectiveness of the presented models is evaluated on a dataset of 92 patients with non-small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy above 80, and highlighted the importance of covariance-based texture aggregation. At the end of the talk, computer tools will be presented to easily extract 3D radiomics quantitative features from PET-CT images.
Ou quelques réflexions autour des comportements d’un leader stratégique qui semblent être sans valeurs mesurables mais qui sont certainement à haute valeur ajoutée pour l’équipe/entreprise/organisation.
Après une courte introduction qui va présenter une définition de leadership stratégique, cet atelier va se baser, comme fil rouge, sur les 10 principes communément admis du leadership stratégique (suite à une large étude de PWC). Pour chacun de ces principes, nous allons interagir avec les participant-e-s tant des comportements à (haute) valeur ajoutée que ceux plutôt toxiques ; puis débattre autour des indicateurs de mesures possibles (ou déjà expérimentés par les participants)
L’objectif principal est que chaque participant-e s’interroge sur son leadership stratégique et la valeur amenée dans l’entreprise/organisation et qu’il-elle soit parfois défié par le regard d’autres participant-e-s.
Presented by Adrien Depeursinge, PhD, at MICCAI 2015 Tutorial on Biomedical Texture Analysis (BTA), Munich, Oct 5 2015.
Texture-based imaging biomarkers complement focal, invasive biopsy based biomarkers by providing information on tissue structure over broad regions, non-invasively, and repeatedly across multiple time points. Texture has been used to predict patient survival, tissue function, disease subtypes and genomics (imagenomics and radiogenomics). Nevertheless, several challenges remain, such as: the lack of an appropriate framework for multi-scale, multi-spectral analysis in 2D and 3D; localization uncertainty of texture operators; validation; and, translation to routine clinical applications.
Social media research in the health domain (tutorial) - [part 1]Luis Fernandez Luque
Tutorial about the use of social media in the health domain. The tutorial is designed for healthcare professionals interested in eHealth. It was done for Weill Cornell Medicine - Qatar.
See the part II of the tutorial here: https://www.slideshare.net/IngmarWeber/social-media-research-and-practice-in-the-health-domain-tutorial-part-ii
Learn more about social media for health here https://www.futurelearn.com/courses/social-media-in-healthcare
Advances and Challenges in Visual Information Search and Retrieval (WVC 2012 ...Oge Marques
Part I – Concepts, challenges, and state of the art
Part II – Medical image retrieval
Part III – Mobile visual search
Part IV – Where is image search headed?
The aim of the 3DOR Workshop series is to stimulate researchers from different fields to present state-of-the-art work in the field. 3DOR 2013 took place as the 6th workshop in this series on May 11, 2013 in Girona (Spain), in conjunction with Eurographics 2013. Prof. Henning Muller presented the keynote talk about Medical 3D data retrieval.
Meeting Federal Research Requirements for Data Management Plans, Public Acces...ICPSR
These slides cover evolving federal research requirements for sharing scientific data. Provided are updates on federal agency responses to the 2013 OSTP memo, guidance on data management plans, resources for data management and curation training for staff/researchers, and tips for evaluating public data-sharing services. ICPSR's public data-sharing service, openICPSR, is also presented. Recording of this presentation is here: https://www.youtube.com/watch?v=2_erMkASSv4&feature=youtu.be
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Maximizing the value of data, computing, data science in an academic medical center, or 'towards a molecularly informed Learning Health System. Given in October at the University of Florida in Gainesville
Big Data in Pediatric Critical Care by Mohit MehraData Con LA
Abstract:- There is an urgent need in the pediatric ICUs to collect, store and transform healthcare data to make accurate and timely predictions in the areas of patient outcomes and treatment recommendations. We are currently heavily invested in using open source big data stacks in order to achieve this goal and help our young ones. In this talk I can highlight how we go about managing structured and unstructured high frequency data generated from a disparate set of devices and systems and ultimately how we have created data pipelines to process the data and make it available for data scientists and app developers.
Prof. Henning Müller gave the presentation Information Access to Medical Image Data: from Big Data to Semantics - Academic and Commercial Challenges at the DBTA Workshop on Academic-Industrial Forms of Collaboration at the University of Lausanne.
The presentation introduced two EU funded projects called Khresmoi and Visceral and highlighted the collaborations of HES-SO with several companies in these projects and also with other Universities and institutions in Switzerland.
This prevention is a reflection of my vision on how Big Data impacts healthcare and the efforts that Oracle and VX Healthcare Analytics put into making Big Data work in the patient profiling space
A l'occasion de la première journée eHealth du 7 juin 2013, Prof. Henning Müller et Prof. Michael Schumacher ont présenté les projets de recherche eHealth de notre institut.
Big Data at Geisinger Health System: Big Wins in a Short TimeDataWorks Summit
Geisinger Health System is well known in the healthcare community as a pioneer in data and analytics. We have had an Electronic Health Record (EHR) since 1996, and an Electronic Data Warehouse (EDW) since 2008. Much of daily and weekly operational reporting, as well as an abundance of ad hoc analytics, come from the EDW.
Approximately 18 months ago, the Data Management team implemented Hadoop in the Hortonworks Data Platform (HDP), and successes in implementation and development have proven to the organization that we should abandon the traditional EDW in favor of the Big Data (HDP) platform.
In less than 18 months, we stood up the platform, created a data ingestion pipeline, duplicated all source feeds from the EDW into HDP, and had several analytics developed with HDP and Tableau. Furthermore, we have exploited the new capabilities of the platform, where we use Natural Language Processing (NLP) to interrogate valuable (but previously hidden) clinical notes. The new platform has data that is modeled and governed, setting the stage to push Geisinger Health System from a pioneer to a leader in Big Data and Analytics.
This session will focus on Hortonworks Data Platform, covering data architecture, security, data process flow, and development. It is geared toward Data Architects, Data Scientists, and Operations/I.T. audiences.
Bio-IT
Cloud Medical Research and Management System
Presentation from the World Vitiligo Symposium 2011 by Mr. Prem S. Couture.
---Media release---
VRF presents software solution for R&D in skin diseases, such as vitiligo or melanoma.
By Yan Valle July 29, 2011 Firenze - VR Foundation released beta version of Cloud Medical Research And Management system, a bio-IT solution for discovery of correlations between multiple types of medical data for R&D activities at II European Congress of Dermatovenerology in St. Petersburg.
The Cloud MRM enables biotechnology research organizations to efficiently discover unseen correlations between clinical data and gene expression patterns while enabling registration of all relevant medical data in support of downstream development and production of therapeutic proteins for skin diseases, such as vitiligo or melanoma cancer.
The Cloud MRM may also be appealing to big pharma in obtaining aggregated, complex data sets for small-molecule R&D processes; or to biotech companies and CROs with limited resources to develop or maintain in-house R&D software.
The system provides functionalities to facilitate data and biological material handovers among researchers from different countries. Cross analysis of de-identified patient profile data along with blood test analysis, biopsy, gene polymorphysms, and also visual data such as images, MRI, CAT scans allows automated discovery of complex relationships between different parameters causing vitiligo or other skin diseases.
Cloud MRM is hosted on a secure private cloud, easily accessible via Internet through a variety of end-user devices, including iPad or Android tablets.
VR Foundation is a non-profit organization expediting vitiligo research globally. For further details, please email Yan@vrfoundation.org.
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First Steps Towards a Risk of Bias Corpus of Randomized Controlled Trials. The risk of bias specifically pertains to systematic errors in the design, conduct, or reporting of a study that can potentially lead to a deviation from the true effect being measured.
Exploiting biomedical literature to mine out a large multimodal dataset of rare cancer studies. Presentation of Anjani K. Dhrangadhariya (Institute of Information Systems, HES-SO Valais-Wallis, Sierre) at SPIE Medical Imaging 2020.
Présentation de Prof. Yann Bocchi de l'institut informatique de gestion HES-SO Valais-Wallis à la Conférence TechnoArk 2020 sur le thème de l'industrie connectée.
Studying Public Medical Images from Open Access Literature and Social Networks for Model Training and Knowledge Extraction
Henning Müller, Vincent Andrearczyk, Oscar Jimenez, Anjani Dhrangadhariya
Maria Tootell (Oprisko)
Risques opérationnels et le système de contrôle interne : les limites d’un tel système
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Cas pratiques issus de la gestion des risques, applicables aux secteurs public ou privé
eGov Workshop – La plus-value du système de contrôle interne
Creating an optimal travel plan is not an easy task, particularly for people with mobility disabilities, for whom even simple trips, such as eating out in a restaurant, can be extremely difficult. Many of their travel plans need to be made days or even months in advance, including the route and time of day to travel. These plans must take into account ways in which to navigate the area, as well as the most suitable means of transportation. In response to these challenges, this study was designed to develop a solution that used linked data technologies in the domains of tourism services and e-governance to build a smart city application for wheelchair accessibility. This smart phone application provides useful travel information to enable those with mobility disabilities to travel more easily.
Dans le cadre des Swiss Mobility Days organisés à Martigny (Suisse) en avril 2016, Yann Bocchi, Prof. à l'institut Informatique de Gestion de la HES-SO Valais-Wallis, présente le projet NOSE (Nomadic, Modular and Scalable IT Ecosystem for Pervasive Sensing).
Mocodis is a web application facilitating the transfer of skills between senior and junior associates. It can be used in companies, institutions to capitalize on the experience of older employees, or can be used to train employees top down. Mocodis automatically generates dynamic micro-courses combining text, audio and video resources, and uses an algorithm to analyze user satisfaction to produce better courses at the next request.
This paper aims at reporting on the findings of two quantitative studies and one qualitative study conducted among HES-SO undergraduate and graduate students. We have outlined the characteristics of the “digital natives” generation of students attending our courses and have submitted a sample of these students to an experiment using the Google Glass, in order to assess whether the use of this new device could meet the students’ expectations for accessing enriched learning resources. This paper also presents some thoughts for consideration regarding future research to be lead in the field of innovative technologies and learning processes
This work presents a data-intensive solution to predict Photovoltaïque energy (PV) production.
PV and other renewable sources have widely spread in recent years. Although those sources provide an environmentally-friendly solution, their integration is a real challenge in terms of power management as it depends on meteorological conditions. The ability to predict those variable sources considering meteorological uncertainty plays a key role in the management of the energy supply needs and reserves.
This paper presents an easy-to-use methodology to predict PV production using time series analyses and sampling algorithms. The aim is to provide a forecasting model to set the day-ahead grid electricity need. This information is useful for power dispatching plans and grid charge control. The main novelties of our approach is to provide an easy implemented and flexible solution that combines classification algorithms to predict the PV plant efficiency considering weather conditions and nonlinear regression to predict weather forecasted errors in order to improve prediction results.
The results are based on the data collected in the Techno-pôle’s microgrid in Sierre (Switzerland) described further in the paper.
The best experimental results have been obtained using hourly historical weather measures (radiation and temperature) and PV production as training inputs and weather forecasted parameters as prediction inputs. Considering a 10 month dataset and despite the presence of 17 missing days, we achieve a Percentage Mean Absolute Deviation (PMAD) of 20% in August and 21% in September. Better results can be obtained with a larger dataset but as more historical data were not available, other months have not been tested.
Switzerland is one of the most desirable European destinations for Chinese tourists, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leading social media platform – Sina Weibo, has more than 600 million users. Weibo’s great market penetration suggests that tourism operators and markets need to understand how to build effective and sustainable communications on Chinese social media platforms. The goal of this research is to understand Chinese tourists’ behaviors and patterns in Switzerland by adopting a linked data approach on Sina Weibo, and to design a decision support system based on the findings.
How social media could be used to interpret the satisfaction of clients visiting a destination based on real use cases?
More than just to communicate with clients, this analysis let the resort analysing the effective tourist needs and hope when he is coming in this tourism hotspot (Mountain Bike, Ski, …). With a semantic approach, it is possible to know what the interests of tourists are when they are travelling in a specific region.
Cette formation a été donnée dans le cadre de la 1ère Université de la Valeur qui s'est tenue à l'Université de Genève du 31 août au 4 septembre 2015.
Après une présentation des principes de la méthodologie agile et des concepts principaux de management agile, les participant-e-s ont effectué un travail de clarification de leurs valeurs et de leur alignement avec celles de leur entreprise.
Plan de la formation :
- Principes de l’agilité
- Concepts de management agile
- Détermination de ses valeurs et de celles de l’entreprise
- Alignement des valeurs
- Exercice de co-création de la suite de la journée (application d’un outil de l’agilité)
In this paper we present a novel technique for characterizing and classifying 3D textured volumes belonging to different lung tissues in 3D CT images.We build a volume based 3D descripton, robust to changes of size, rigid spatial transformations and texture variability, thanks to the integration of Riesz-wavelet features within a Covariance-based descriptor formulation. 3D Riesz features characterize the morphology of tissue density thanks to their response to changes in intensity in CT images. These features are encoded in a Covariancebased descripton formulation: this provides a compact and flexible representation thanks to the use of feature variations rather than dense features themselves, and adds robustness to spatial changes. Furthermore, the particular symmetric definite positive matrix form of these descriptors causes them to lay in a Riemannian manifold. Thus, descriptors can be compared with analytical measures, and accurate techniques from Machine Learning and clustering can be adapted to their spatial domain. Additionally we present a classification model following a “Bag of Covariance Descriptors” paradigm in order to distinguish three different nodule tissue types in CT: solid, ground-glass opacity (GGO), and healthy. Classification accuracy is estimated based on an acquired dataset of 95 patients with manually delineated ground truth by radiology specialists in 3D. The promising outcomes of the presented method support a future aim for automated lung nodule detection and computerized diagnosis assistance applications.
One approach to computerized histopathology image analysis is to leverage the multi-scale texture information resulting from single nuclei appearance to entire cell populations. In this talk, we will introduce a novel framework for learning highly adaptive texture-based local models of biomedical tissue. I will discuss our initial experience with the differentiation of brain tumor types in digital histopathology.
Pour bon nombre d’entreprises, la révolution numérique implique de réinventer leur organisation. Dans ce contexte complexe, l’orateur s’interroge sur la nécessité d’aligner les valeurs entre les différents acteurs (métiers, DSI, autres) pour rendre la coopération plus aisée entre eux.
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HOT NEW PRODUCT! BIG SALES FAST SHIPPING NOW FROM CHINA!! EU KU DB BK substit...GL Anaacs
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Protonitazene (hydrochloride) CAS: 119276-01-6
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We specializes in exporting high quality Research chemical, medical intermediate, Pharmaceutical chemicals and so on. Products are exported to USA, Canada, France, Korea, Japan,Russia, Southeast Asia and other countries.
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This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
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Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
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Medical image analysis, retrieval and evaluation infrastructures
1. Medical image analysis, retrieval and
evaluation infrastructures
Henning Müller
HES-SO VS &
Martinos Center
2. Overview
• Medical image retrieval projects
• Image analysis and 3D texture modeling
• Data science evaluation infrastructures
– ImageCLEF
– VISCERAL
– EaaS – Evaluation as a Service
• What comes next?
3. Henning Müller
• Studies in medical informatics in
Heidelberg, Germany
– Work in Portland, OR, USA
• PhD in image processingin Geneva,
focus on image analysis and retrieval
– Exchange at Monash Uni., Melbourne, Australia
• Prof titulaire at UNIGE/HUG in medicine (2014)
– Medical image analysis and retrieval for decision
support
• Professor at the HES-SO Valais (2007)
– Head of the eHealth unit
• Sabbaticalat the Martinos Center, Boston, MA
4. Medical image retrieval (history)
• MedGIFT project started in 2002
– Global image similarity
• Texture, grey levels
– Teaching files
– Linking text files and
image similarity
• Often data not available
– Medical data hard to get
– Images and text are
connected in cases
• Unrealistic expectations, high quality vs. browsing
– Semantic gap
5. Medical imaging is big data!!
• Much imaging datais produced
• Imaging data is very complex
– And getting more complex
• Imaging is essential for
diagnosis and treatment
• Images out of their context
loose most of their sense
– Clinical data are necessary
– Diagnoses often not precise
• Evidence-based medicine&
case-basedreasoning
10. Texture analysis (2D->3D->4D)
• Describe various medical tissue types
– Brain, lung, …
– Concentration on 3D and 4D data
– Mainly texture descriptors
• Extract visual features/signatures
– Learned, so relation to deep learning
Adrien Depeursinge, Antonio Foncubierta–Rodriguez, Dimitri Van de Ville, and Henning
Müller, Three–dimensional solid texture analysis and retrieval: review and opportunities,
Medical Image Analysis, volume 18, number 1, pages 176-196, 2014.
11. Database with CT image of
interstitial lung diseases
• 128 cases with CT image series and biopsy
confirmed diagnosis
• Manually annotated regions for tissue classes (1946)
– 6 tissue types of 13 with a larger number of examples
• 159 clinical parameters extracted (sparse)
– Smoking history, age, gender,
hematocrit, …
• Availableafter signature of a
license agreement
12. Learned 3D signatures
• Learn combinations of Riesz wavelets as digital
signatures using SVMs (steerable filters)
– Create signatures to detect small local lesions
and visualize them
Adrien Depeursinge, Antonio Foncubierta–Rodriguez, Dimitri Van de Ville, and Henning
Müller, Rotation–covariant feature learning using steerable Riesz wavelets, IEEE
Transactions on Image Processing, volume 23, number 2, page 898-908, 2014.
13. Learning Riesz in 3D
• Most medical tissues are naturally 3D
• But modeling gets much more complex
– Vertical planes
– 3-D checkerboard
– 3-D wiggled
checkerboard
15. • Benchmark on multimodal imageretrieval
– Run since 2003, medical task since 2004
– Part of the Cross language evaluation forum
• Many tasks related to image retrieval
– Image classification
– Image-based retrieval
– Case-based retrieval
– Compound figure separation
– Caption prediction
– …
• Many old databases remain available, imageclef.org
18. Test DataTraining Data
Participants Organiser
Participant
Virtual
MachinesRegistration
System
Annotation
Management System
Analysis
System
Annotators
(Radiologists)
Locally Installed
Annotation
Clients
Microsoft
Azure
Cloud
Test Data
19. Evaluation as a Service (EaaS)
• Moving the algorithms to the data not vice versa
– Required when data are: very large, changing
quickly, confidential (medical, commercial, …)
• Different approaches
– Source code submission, APIs, VMs local or in the
cloud, Docker containers, specific frameworks
• Allows for continuous evaluation, component-
based evaluation, total reproducibility, updates, …
– Workshop March 2015 in Sierre on EaaS
– Workshop November 2015 in Boston on cloud-
based evaluation
20. Sharing images, research data
• Very important aspect of research is to have solid
methods, data, large if possible
– If data not available, results can not be reproduced
– If data are small, results may be meaningless
• Many multi-center projects spend most money on
data acquisition, often delayed no time for analysis
– IRB takes long, sometimes restrictions are strange
• Research is ineternational!
• NIH & NCI are great to push data availability
– But data can be made available in an unusable way
26. Business models for these links
• Manually annotate large data sets for challenges
– Data needs to be available in a secure space
• Have researcher work on data (on infrastructure)
– Deliver code
• Commercialize results and share benefits
27. Future of research infrastructures
• Much more centered around data!!
– Nature Scientific Data underlines the importance!
• Data need to be available but in a meaningful way
– Infrastructure needs to be available and way to
evaluate on the data with specific tasks
• More work for data preparation but in line with IRB
– Analysis inside medical insitutions
• Code will become even more portable
– Docker helps enormously and develops quickly
• Public private partnerships to be sustainable
• Total reproducibility, long term, sharing tools
• Much higher efficiency
28. • Part of QIN – Quantitative Imaging Network (NCI)
• Create challenges for QIN to validate tools
• Use Codalabto run project challenges
– Run code in containers (Docker), well integrated
• Automate as much as possible
– Share code blocks across teams, evaluate
combinations
29. Conclusions
• Medicine is (becoming) digital medicine
– More data and more complex links (genes, visual,
signals, …)
• Medical data science requires new infrastructures
– Use routine data, not manually extracted, curated
data, curate large scale, accommodate for errors
– Use large data sets from data warehouses
– Keep data where they are produced
• More “local” computation, so where data are
– Secure aggregation of results
• Sharing infrastructures, data and more
30. Contact
• More information canbe found at
– http://khresmoi.eu/
– http://visceral.eu/
– http://medgift.hevs.ch/
– http://publications.hevs.ch/
• Contact:
– Henning.mueller@hevs.ch