The CLEF2016 Working Notes are now on-line on CEUR-WS Proceedings: http://ceur-ws.org/Vol-1609/
The Working Notes correspond to the Conference and Labs of the Evaluation forum that took place in Évora, Portugal, 5-8 September, 2016.
The CLEF2016 Working Notes are now on-line on CEUR-WS Proceedings: http://ceur-ws.org/Vol-1609/
The Working Notes correspond to the Conference and Labs of the Evaluation forum that took place in Évora, Portugal, 5-8 September, 2016.
Adrien Depeursinge will be a speaker at the MIT CSAIL Seminar Series: Biomedical Imaging and Analysis 2015/2016. The topic of his talk is ‘Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification’. The presentation will take place Thursday, July 28, 2016.
SUMMARY
We present texture operators encoding class-specific local organizations of image directions (LOID) in a rotation-invariant fashion. The LOIDs are key for visual understanding, and are at the origin of the success of the popular approaches such as local binary patterns (LBP) and the scale-invariant feature transform (SIFT). Whereas LBPs and SIFT yield handcrafted image representations, we propose to learn data-specific representations of the LOIDs in a rotation-invariant fashion. The image operators are based on steerable circular harmonic wavelets (CHW), offering a rich and yet compact initial representation for characterizing natural textures. The joint location and orientation required to encode the LOIDs is preserved by using moving frames (MF) texture representations built from locally-steered multi-order CHWs. In a second step, we use support vector machines (SVM) to learn a multi-class shaping matrix of the initial CHW representation, yielding data-driven MFs that are invariant to rigid motions. We experimentally demonstrate the effectiveness of the proposed operators for classifying natural textures.
Henning Müller was teaching in a workshop of the Harvard/MIT Health Science and Technology program at MIT on July 13. The workshop was on Topics in clinical imaging informatics: beyond the radiology report.
The Ninapro project has the following updates:
1) A Ninapro summary paper was accepted on the Italian Journal of Hand Surgery and it will be published in September. If needed, you can download the paper from the following link: https://www.dropbox.com/s/w8fxhbthzmljhg6/Atzori_RCM_2016.doc?dl=0
2) 2) An electronic poster was presented the XXI Congress of the Federation of European Societies for Surgery of the Hand, in Santander: http://publications.hevs.ch/index.php/publications/show/2095
3) The paper ‘Clinical Parameter Effect on the Capability to Control Myoelectric Robotic Prosthetic Hands’ by Manfredo Atzori et al. was published in May in the Journal of Rehabilitation Research and Development: http://publications.hevs.ch/index.php/publications/show/1981
The paper ‘Cloud-based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks’ by Oscar Jimenez-del-Toro et al. is now available online in IEEE Xplore.
More information about the VISCERAL project can be found at www.visceral.eu
ABSTRACT
Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.
The 14th International Workshop on Content-based Multimedia Indexing , June 15-17 2016, took place in Bucharest, Romania. The event was a success and brough together various communities involved in all aspects of content-based multimedia indexing for retrieval, browsing, visualization and analytics.
The VISCERAL project received a Microsoft Azure sponsorship until June 2017. This will allow us to continue running the VISCERAL benchmarks in the Azure cloud platform for an extended period of time.
The current open benchmarks are:
Anatomy3 continuous: The Benchmark tasks are the segmentation of bones, inner organs and relevant substructures. The Benchmark runs under a continuous evaluation approach – results on the test set can be obtained at any time.
Detection2: The goal is to automatically detect lesions in images. We will distribute training data with a large number of expert annotated lesions for training of detection algorithms. During the evaluation these algorithms will be applied to new data. The lesions are in bones, lungs, brain, liver, lymphnodes.
Retrieval2: The objective of retrieval algorithms will be to find cases with similar anomalies based on query cases.
The article “Optimized Distributed Hyperparameter Search and Simulation for Lung Texture Classification in CT Using Hadoop” by Roger Schaer et al. is now available online: http://www.mdpi.com/2313-433X/2/2/19/html
Abstract
Many medical image analysis tasks require complex learning strategies to reach a quality of image-based decision support that is sufficient in clinical practice. The analysis of medical texture in tomographic images, for example of lung tissue, is no exception. Via a learning framework, very good classification accuracy can be obtained, but several parameters need to be optimized. This article describes a practical framework for efficient distributed parameter optimization. The proposed solutions are applicable for many research groups with heterogeneous computing infrastructures and for various machine learning algorithms. These infrastructures can easily be connected via distributed computation frameworks. We use the Hadoop framework to run and distribute both grid and random search strategies for hyperparameter optimization and cross-validations on a cluster of 21 nodes composed of desktop computers and servers. We show that significant speedups of up to 364× compared to a serial execution can be achieved using our in-house Hadoop cluster by distributing the computation and automatically pruning the search space while still identifying the best-performing parameter combinations. To the best of our knowledge, this is the first article presenting practical results in detail for complex data analysis tasks on such a heterogeneous infrastructure together with a linked simulation framework that allows for computing resource planning. The results are directly applicable in many scenarios and allow implementing an efficient and effective strategy for medical (image) data analysis and related learning approaches.
The ‘Report on the Cloud-based Evaluation Approaches Workshop 2015’ by Henning Müller et al. is now available online: http://sigir.org/files/forum/2016J/p035.pdf
ABSTRACT
Data analysis requires new approaches in many domains for evaluating tools and tech- niques, particularly when the data sets grow large and more complex. Evaluation–as–a– service (EaaS) was coined as a term to represent evaluation approaches based on APIs, virtual machines or source code submission, different from the common paradigm of evalu- ating techniques on a distributed test collection, tasks and submitted results files. Such new approaches become necessary when data sets become extremely large, contain confidential information or might change quickly over time. The workshop on cloud–based evaluation (CBE) took place in Boston, MA, USA on November 5, 2015 and explored several approaches for data analysis evaluation and frameworks in this field. The objective was to include several stakeholders from academic partners, companies to funding agencies to cover various inter- ests and viewpoints in the discussion of evaluation infrastructures. The workshop focused on the biomedical domain but the results are easily applicable to many domains of information analysis and retrieval.
Prof. Henning Müller is Series Editor of the new Springer series Brain Informatics and Health. The website of the book is now online.
The Brain Informatics & Health (BIH) book series addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics as well as topics relating to brain health, mental health and well-being. It also welcomes emerging information technologies, including but not limited to Internet of Things (IoT), cloud computing, big data analytics and interactive knowledge discovery related to brain research. The BIH book series also encourages submissions that explore how advanced computing technologies are applied to and make a difference in various large-scale brain studies and their applications.
The second edition of the ‘ArkAthon Hacking Health Valais‘ will take place 3-5 june 2016 at Sierre, Switzerland
This event will start at the end of the eHealth Day (Friday, June 3). Participating teams will have 48 hours to solve the challenges on the platform trying to reach concrete results and proposals. At its conclusion, a jury will decide on the projects presented in the competition. The winning project will win the main prize with a total value of CHF 25,000.
The paper ‘Medical image modality classification using discrete Bayesian networks’ by Jacinto Arias, Jesus Martínez-Gómez, Jose A. Gámez, Alba G. Seco de Herrera and Henning Müller is now publised online.
ABSTRACT
In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifiers used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes.
A request to the Hasler Foundation has been approved for the project ELGAR PRO – Electromyography, Gaze & Artificial-intelligence for Prosthetic Robustness. The approved amount is CHF 44,460.
Prof. Henning Müller is included among the organizers of Open Science Prize projects. The Open Science Prize is a partnership between the Wellcome Trust, the US National Institutes of Health (NIH) and the Howard Hughes Medical Institute to unleash the power of open content and data to advance biomedical research and its application for health benefit.
The deepPMC project uses the Open PubMed Central content to create deep training data sets for machine learning and image analysis. The goals of this pilot project would be to
i) enrich the metadata of open access content of PMC focusing on the histopathology image data by organizing challenges for crowdsourcing of this task
ii) develop tools that give access to the data to develop cohorts for machine learning as well as to support information retrieval of images or articles based on visual and textual information.
A news report about the project Megane Pro was presented in Canal9, Switzerland. It contains a short interview with Manfredo Atzori:
http://canal9.ch/megane-pro-la-hes-so-et-le-valais-a-la-pointe-de-le-sante/
The project MEGANE PRO funded by the Swiss National Science Foundation (SNF) was launched Monday 8 February 2016 with a meeting in Sierre, Switzerland. The project is lead by Prof. Henning Müller with the collaboration of the University Hospital of Zurich, the IDIAP Research Institute and the University La Sapienza in Rome.
The news were announced here: http://business24.ch/2016/02/09/projekt-zur-steuerung-von-handprothesen-im-wallis/
A Special Issue on Medical Information Retrieval has been published online. This issue from the Information Retrieval Journal was edited by Lorraine Goeuriot, Gareth J.F. Jones, Liadh Kelly, Henning Müller and Justine Zobel.
‘Medical information search refers to methodologies and technologies that seek to improve access to medical information archives via a process of information retrieval (IR). Such information is now potentially accessible from many sources including the general web, social media, journal articles, and hospital records. Health-related content is one of the most searched-for topics on the internet, and as such this is an important domain for IR research. Medical information is of interest to a wide variety of users, including patients and their families, researchers, general practitioners and clinicians, and practitioners with specific expertise such as radiologists.’
Prof. Henning Müller will present ‘Medical image analysis and big data evaluation infrastructures’ at the Integrative Biomedical Imaging Informatics (IBIIS) group at Stanford.
The presentation is part of the 2016 Seminar Series and will take place the 23rd march 2016:http://ibiis.stanford.edu/events/seminars/seminars2016.html
The paper ‘Medical information retrieval: introduction to the special issue’ by Lorraine Goeuriot, Gareth J.F. jones, Liadh Kelly, Henning Müller and Justin Zobel is now available online: http://link.springer.com/article/10.1007/s10791-015-9277-8?wt_mc=internal.event.1.SEM.ArticleAuthorOnlineFirst
“Medical information search refers to methodologies and technologies that seek to improve access to medical information archives via a process of information retrieval (IR). Such information is now potentially accessible from many sources including the general web, social media, journal articles, and hospital records. Health-related content is one of the most searched-for topics on the internet, and as such this is an important domain for IR research. Medical information is of interest to a wide variety of users, including patients and their families, researchers, general practitioners and clinicians, and practitioners with specific expertise such as radiologists…”
Matteo Cognolato has joined the Medgift group.
Welcome to him!