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Starting in Fall 2023, the monthly NCI Imaging Informatics Webinar will = be organized by the Cancer Imaging Program (CIP).
Information about upcoming webinars will also be distributed via the Goo= gle group.
This wiki page will continue to provide slides and recordings for all we= binars between 2012 and April 2023.
Presentations can be found at SlideShare. Some docu= ments on this page are not Section 508 compliant. To receive a compliant do= cument, please email NCI Application Support.
Date | Agenda | Recording |
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AI in Cardiovascular Imaging= Dr. Tim Leiner (Mayo Clinic) Machine learning= and especially deep learning hold great promise to improve patient care. I= n several domains, algorithms perform as good as or better than fellowship = trained radiologists for identification of abnormalities in clinically acqu= ired images. However, there are much broader applications beyond image anal= ysis such as patient selection and examination scheduling, image acquisitio= n and reconstruction, using image data for prognostic purposes, and combing= image data with information from electronic health records, laboratory and= genetic data. Furthermore, in order for algorithms to be broadly accepted,= there are many scenarios where it is important for the clinician that resu= lts are explainable. In addition, clinical deployment and workflow should b= e taken into consideration when designing the algorithm and bringing it to = clinical practice. In my lecture I will focus on these aspects from a cardi= ovascular imaging perspective.
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ChatGPT and the potential healthcare = implications of large language models George Shih, MD, = Weill Cornell Medicine ChatGPT has exploded into our world and it has= the potential to be a widely available near omniscient AI, including many = applications in healthcare for providers, patients, researchers, educators,= students, and healthcare companies. In this talk, we'll explore exam= ples of ChatGPT in healthcare, and discuss the potential impact and implica= tions to stakeholders as ChatGPT evolves and improves over time. |
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Radiation Dose Reduction in CT and it= s Effects on Quantitative Imaging and Machine Learning/Artificial Intellige= nce Algorithms Dr. Michael McNitt-Gray, UCLA CT is wid= ely used in clinical practice with applications ranging from early detectio= n (screening), characterization (diagnosis) and assessment of response to t= herapy. There has been widespread concern over the radiation dose associate= d with these scans, especially on pediatric patients or patients who get fr= equent scans. There have been significant developments which allow the redu= ction of radiation dose from CT including developments in automatic exposur= e control, advanced image reconstruction techniques , more efficient detect= or technologies among others that promise significant radiation dose reduct= ions to patients, while maintaining clinical image quality. While the= se technologies are exquisite and should be investigated wherever possible = in a clinical environment, their effects on quantitative measures extracted= from CT images and machine learning algorithms have not been well characte= rized. These technologies may affect image quality in ways that may limit t= he generalizability of quantitative imaging and Artificial Intelligence/Mac= hine Learning (AI/ML) methods. For example, advanced image reconstruc= tion methods may be able to mitigate the increase in noise that is incurred= when radiation dose is reduced, but there may be some impact on image reso= lution. In addition, many of these techniques are non-linear and adaptive t= o the local image anatomy and pathology, their impacts may be difficult to = predict from application to application and even patient to patient (and ye= s, even within a patient). This presentation will provide examples of these= effects and discuss possible methods to mitigate these effects, which hope= fully will enable more generalizable deployment of quantitative imaging met= hods and AI/ML algorithms. |
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Report of= the MIDI Task Group about best practices and recommendations for medical i= maging de-identification Dr. Dav= id Clunie (Chairperson of the MIDI Task Group) |
<= a href=3D"/download/attachments/362972750/MIDI_TG_Report_ImagingCommunityCa= ll_20221205.pdf?version=3D1&modificationDate=3D1670863882000&api=3D= v2" data-linked-resource-id=3D"515637959" data-linked-resource-version=3D"1= " data-linked-resource-type=3D"attachment" data-linked-resource-default-ali= as=3D"MIDI_TG_Report_ImagingCommunityCall_20221205.pdf" data-nice-type=3D"P= DF Document" data-linked-resource-content-type=3D"application/pdf" data-lin= ked-resource-container-id=3D"362972750" data-linked-resource-container-vers= ion=3D"300">Slides |
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= Bringing AI from Hype to Reality for Routine Clinical Practice: Defin= ing and Addressing the Gaps Dr. Eliot Siegel, University= of Maryland Despite the ever-increasing number of publicly available= imaging databases and oncology AI/Radiomics applications that have been cu= rated and developed over the past more than 15 years, an extraordinarily sm= all number of AI applications are available and in use for routine clinical= cancer care by radiologists, oncologists, and other healthcare providers.&= nbsp; This is the case despite large and carefully and expertly curated and= annotated databases which have been generously funded and made available b= y NCI and other organizations. Mammography CAD/AI has a particularly = interesting and unique history and adoption curve and while it is in widesp= read use throughout the US, there continues to be a large gap in accuracy b= etween the small percentage of studies interpreted by subspecialist mammogr= aphers and the vast majority of studies interpreted by general radiologists= . This presentation will discuss some of the reasons for this continu= ing gap and lack of adoption of mammograph CAD into clinical decision makin= g. Additionally, a combination of regulatory challenges, the lack of = a paradigm for training on datasets consisting of both prior and follow-up = studies, brittleness of algorithms that are not adaptive, bias due in part = to lack of transparency of databases used to develop AI apps, lack of stand= ards for consumption of on prem and off prem algorithms, multiple platforms= for packaging and using applications and lack of post-market surveillance,= questions about whom the algorithms should be designed for, and many other= factors have hampered widespread adoption. This presentation will di= scuss some solutions to these challenges that could accelerate adoption of = these algorithms which could substantially enhance care for oncology patien= ts. |
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NCI's Imaging Data Commo= ns: Fall 2022 Update Andrey Fedorov, Ph.D., Harva= rd The NCI's Imaging Data Common= s (IDC) is a cloud-based repository of publicly available cancer imaging da= ta co-located with the analysis and exploration tools and resources. IDC is= a node within the broader NCI Cancer Research Data Commons (CRDC) infrastr= ucture that provides secure access to a large, comprehensive, and expanding= collection of cancer research data. In this presentation we will cover the= highlights of IDC development that took place since the production release= of the repository. Among other updates, we will discuss the new datasets t= hat have been released by IDC, new features of the platform, and the ongoin= g work on expanding the learning materials, including the application of ID= C and cloud computing to support reproducible AI research. Hugo Aerts, Ph.D., Harvard MGB Recent advances in artificial intelligence in medicine have led to a= profusion of studies that apply deep learning to problems in radiology and= pathology, among others. However, the effective dissemination of deep lear= ning algorithms remains challenging, inhibiting reproducibility and benchma= rking studies, impeding further validation, and ultimately hindering their = effectiveness in the cumulative scientific progress. In this talk, we will = discuss a platform we are developing for the structured dissemination of de= ep learning models that is domain-, data-, and framework-agnostic, and can = cater to different workflows and contributors=E2=80=99 preferences. Ultimat= ely, these efforts will bring much-needed transparency to AI and accelerate= scientific discoveries, academic training, and clinical adoption of AI app= lications in medicine. |
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Bridging the gap between prostate rad= iology and pathology through machine learning Mirabela Rusu,= PhD, Stanford University The subtle d= ifference in MRI appearance of prostate cancer and benign prostate tissue r= enders the interpretation of prostate MRI challenging, causing many false p= ositives, false negatives, and wide variations in interpretation. My labora= tory focuses on improving the interpretation of prostate MRI by developing = deep learning models that automatically localize indolent and aggressive pr= ostate cancers on MRI scans. The novelty of our methods comes from using wh= ole-mount pathology images to label MRI images and to create pathomic MRI b= iomarkers of aggressive and indolent cancers. Our approach achieved an area= under the receiver operator characteristics curve of 0.93 evaluated on a p= er-lesion basis and outperformed existing deep learning models. In patients= outside our training cohorts, such predictive models will outline the exte= nt of cancer on radiology images in the absence of pathology images, thus h= elping guide the prostate biopsy and local treatment. The talk will focus on discussing recent contributions from= my lab on registering whole-mount pathology images with MRI, training deep= learning models to extract pathomic MRI biomarkers and using them in train= ing deep learning models to detect and distinguish indolent and aggressive = prostate cancers on MRI, and showing the benefits of using labels from path= ology in training deep learning models to distinguish indolent from aggress= ive prostate cancer on MRI. |
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Machine Intelligence/Data Science in = Medical Imaging of Breast Cancer and COVID-19 Maryellen Gige= r, Ph.D, University of Chicago Artific= ial Intelligence in medical imaging involves research in task-based discove= ry, predictive modeling, and robust clinical translation. Quantitativ= e radiomic analyses, an extension of computer-aided detection (CADe) and co= mputer-aided diagnosis (CADx) methods, are yielding novel image-based tumor= characteristics, i.e., signatures that may ultimately contribute to the de= sign of patient-specific cancer diagnostics and treatments. Beyond human-en= gineered features, deep convolutional neural networks (CNN) are being inves= tigated in the diagnosis of disease on radiography, ultrasound, and MRI.&nb= sp; The method of extracting characteristic radiomic features of a lesion a= nd/or background can be referred to as =E2=80=9Cvirtual biopsies=E2=80=9D.&= nbsp; Various AI methods are evolving as aids to radiologists as a second r= eader or a concurrent reader, or as a primary autonomous reader. In a= ddition, performance evaluations, as well as considerations of robustness a= nd repeatability, are necessary to enable translation. This presentation wi= ll discuss the development, validation, database needs, and ultimate future= implementation of AI in the clinical radiology workflow including examples= from breast cancer and COVID-19. In addition, aspects of MIDRC (midrc.org= ) will be discussed. |
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AI/ML Trends in Oncology and the Rugg= ed Path Towards the Clinic Issam El Naqa, PhD, Moffit Cancer= Center Artificial intelligence = (AI) and Machine learning (ML) algorithms are currently transforming biomed= ical research, especially in the context of cancer research and clinical ca= re. Despite the tremendous potentials in automating workflow, personalizing= care, and reducing health disparity, to name a few prospects, their applic= ation in oncology and healthcare has been limited in scope with less than 5= % of major healthcare providers implementing any form of AI/ML solutions. T= his can be attributed to multitude of concerning issues regarding the deplo= yment of AI/ML driven technologies into the clinic. These concerns include = but not limited to skepticism related to commercialization hype, under repr= esentative training data, inherent implementation bias, lack of robustness = and absence of prediction transparency. In this work, we will discuss some = of these impending challenges and highlight different approaches for detect= ing and mitigating such bias in implementing clinical AI/ML algorithms. We = further show examples of applying these approaches in oncology applications= from our work and others and discuss their implications to pave the way fo= r AI/ML in clinical practice. |
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February 7, 2022 | Digital twins for oncology via imaging-based mathematical modeling Thomas Yankeelov, PhD= , University of Texas Austin Our lab i= s focused on integrating quantitative imaging data with mechanism-based, ma= thematical models to predict treatment response. In this presentation= , we will discuss some of our preliminary efforts at building digital twins= to achieve this goal. We will begin by considering the I-SPY trials = for breast cancer as a specific example of how the success of adaptive, population-based clinical trials indicate that digital twins can lead= to the success of adaptive, individual-based, clinical trials.&nb= sp; Then we will emphasize the importance of physics and biology-based math= ematical models for constructing digital twins. Finally, we will illustrate= how these ideas are beginning to play out in predicting and optimizing neo= adjuvant therapy for breast cancer. |
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December 6, 2021 |
Precision Medicine Approach to Breast= Cancer Detection and Diagnosis Martin Yaffe, PhD, Sunnybroo= k Research Institute and The University of Toronto Dr. Yaffe will describe a multi-platform approach under invest= igation in his lab to improve the effectiveness of breast cancer detection = and diagnosis. We are developing radiomic tools to guide the stratification= of women for breast cancer screening that will be more accurate and effici= ent in detection than the =E2=80=9Cone size fits all=E2=80=9D use of mammog= raphy whose accuracy suffers, particularly in dense breasts. We employ micr= osimulation modeling to guide that work. We are also exploring the integrat= ion of radiomic information from in vivo medical images with histopathology= , single-cell multiplex biomarker analysis, and targeted molecular sequenci= ng to better characterize breast and other cancers and their immune environ= ment and to explore their spatial heterogeneity. |
Transcript (txt)<= /p> |
November 1, 2021 | NCI CCR Artificial Intelligence = Resource: Recent AI Applications in Cancer Imaging Presenter=
s: Artificial Intelligence (AI) is becoming important for cancer res= earch but is difficult to access for most labs. In 2020, the NCI Center for= Cancer Research (CCR) created a new AI Resource (AI) to benefit researcher= s in the CCR. The group focuses on translational computer vision approaches= to analyzing medical images, such as radiologic, digital pathology, video/= endoscopy and optical imaging, among others. Examples of potential pr= ojects include developing better screening, detection methods or predictive= markers, or improving procedures among many others. With experts in pathology, medical imaging, and machine lear= ning, AIR has taken on a diverse portfolio of research projects in their fi= rst year. In this seminar, senior members of the group will discuss its for= mation, collaboration experience, recent progress, and challenges for deplo= ying developed models back to the hands of researchers across varying domai= ns in NCI. |
The presentation contained unpublished data.<= /p> As projects are finished and code is released, the AIR team will upda= te the webpage. https://ostr.ccr.can= cer.gov/emerging-technologies/air/ You can also email the AIR tea= m with any questions you might have (air@nih.gov) |
October 4, 2021 | Multiplexed Tissue Imaging to Study C= ancer Sandro Santagata, MD PhD Our team at the Harvard Medical School=E2=80=99s Lab of Systems Pha= rmacology has generated reagents, workflows, and data analysis/visualizatio= n approaches for multiplexed tissue imaging. We developed tissue-based cycl= ic immunofluorescence (t-CyCIF) for subcellular imaging of formalin-fixed a= nd paraffin-embedded (FFPE) and frozen tissues across 20-60 different prote= ins markers from a single tissue section. To support the use of multiplexed= tissue imaging in the NCI Human Tumor Atlas Network (HTAN), we have develo= ped algorithms and workflows to analyze these complex images, digital docen= ts for their narrated viewing, and reporting standards for public data shar= ing. The information from these imaging methods complement data acquired by= microregion spatial transcriptomics technologies. We have also used high-r= esolution imaging of tissues to identify functional interactions (e.g., imm= une synapses) in cancer tissues and have created multiplexed 3D cancer atla= ses to more completely characterize the architecture of the tumor-immune la= ndscape in colon cancer and in melanomas, from pre-cancer lesions through m= etastasis. Imaging Data Commons Production Release Update Andrey Fedorov, PhD The Na= tional Cancer Institute (NCI) Cancer Research DataCommons (CRDC) aims to es= tablish a national cloud-based data science infrastructure. The goal of IDC= is to enable a broad spectrum of cancer researchers, with and without imag= ing expertise, to easily access and explore the value of deidentified imagi= ng data and to support integrated analyses with non-imaging data. We achiev= e this goal by co-locating versatile imaging collections with cloud-based c= omputing resources and data exploration, visualization, and analysis tools.= The IDC pilot was released in October 2020. In this presentation, we will = give a brief overview of the capabilities of the production release of the = IDC platform, and discuss the next steps for the development. |
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June 7, 2021 | Medical Imaging De-Identification Ini= tiative (MIDI) A DICOM dataset for evaluation of medical image=
de-identification Dr. Fred Prior (UAMS= ) A growing number of tools and proced= ures claim to properly de-identify image data. Based on our decade of exper= ience managing the Cancer Imaging Archive (TCIA) on behalf of NCI, we devel= oped a DICOM dataset that can be used to evaluate the performance of de-ide= ntification algorithms. DICOM objects were selected from datasets publ= ished in TCIA. Synthetic Protected Health Information (PHI) was gener= ated and inserted into selected DICOM Attributes to mimic typical clinical = imaging exams. The DICOM Standard and TCIA curation audit logs guided the i= nsertion of synthetic PHI into standard and non-standard DICOM data element= s. An answer key was created based on our knowledge of the placement of syn= thetic data and the DICOM standard=E2=80=99s guidelines for what actions sh= ould be taken in regard to the synthetic PHI. A TCIA curation team te= sted the utility of the evaluation dataset and answer key. Medical Image De-Identification using Cloud Se= rvices Dina Mikdadi, Dr. Benjamin Kopchick Patient privacy rules require the removal of Prot= ected Health Information (PHI) before sharing images publicly. Manual de-id= entification is no longer scalable due to the rapid increase in imaging dat= a volume. Our goal was to configure and test the efficacy of a cloud servic= e for automated medical image de-identification (MIDI). One such serv= ice for DICOM images is Google Cloud Platform=E2=80=99s Healthcare API. Tra= ining and test datasets for validation of image de-identification, specific= ally prepared by the placement of synthetic PHI in DICOM headers and image = pixel data, were obtained from The Cancer Imaging Archive. The customized M= IDI pipeline correctly performed 99.8% of expected actions on DICOM header = data elements. For image pixel data, one false-positive case was noted, whi= le all sensitive information was correctly removed from image pixel data. T= hroughput averaged at 58.4 images per second. This implementation of the MI= DI pipeline holds promise for automated de-identification at scale. However= , verification by a human expert is still currently recommended. |
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May 3, 2021 | Establishing next generation dynamic = susceptibility contrast MRI based biomarkers for neuro-oncologic applicatio= ns Dr. Chad Quarles, PhD Dynamic susceptibility contrast (DSC) MRI is one of the most widely used = physiologic imaging techniques in neuro-oncology, enabling the differentiat= ion of glioma grades, identification of tumor components in non-enhancing g= lioma, reliable detection of recurrence, and early detection of therapy res= ponse. This presentation will highlight how an improved understanding of th= e biophysics of the contrast mechanisms underlying DSC-MRI enabled the rece= nt standardization of acquisition protocols for multi-site clinical trials,= is leading to the field=E2=80=99s first benchmark for software validation,= and is informing the development of advanced pulse sequences and analysis = strategies tailored to specific clinical challenges faced in the management= of brain cancer patients. |
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April 5, 2021 | Can you sue an algorithm? Dr. Saurabh Jha (University of Pennsylvania, Perelman School of Medicin= e) |
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March 1, 2021 | Precision Surgery: Intraoperative mol= ecular imaging to improve margin detection Dr. Eben Rosentha=
l, Professor of Otolaryngology, Head & Neck Surgery and Radiology, Stan=
ford University C= ancer is nearly always a surgically treated disease. Almost 80% of patients= with early stage solid tumors undergo surgery at some point within their t= reatment course. A major gap in quality of care remains the high rate of tu= mor-positive margins in head and neck cancer (HNC) following surgical resec= tions. Positive margin rates are directly correlated with lower survival bu= t have remained unchanged at 25% for the last two decades! Primary factors = that have impeded improving the rate of tumor-positive margins include subj= ective surgeon assessment as well as the limited amount of the tissue that = can be sampled for intraoperative frozen-section analysis. We have demonstr= ated that use of intraoperative molecular imaging (IMI) can objectively ide= ntify the area on the tumor specimen most likely to contain a tumor-positiv= e margin (=E2=80=9Csentinel margin=E2=80=9D). In a prospective evaluation, = a fluorescently-labeled tumor-specific contrast agent is administered intra= venously to the patient several days prior to surgery. After the surgical r= esection, the specimen is evaluated with IMI, in which near infrared imagin= g is used to identify the location of the sentinel margin on the surgical s= pecimen. This evaluation is compared to subjective assessments of the deep = tumor margin by palpation, considered the standard of care. It is expected = that IMI imaging will be more accurate in identifying the sentinel margin, = and will shorten the time to histological diagnosis while maintaining tissu= e orientation and high histological image quality. The translation of these= new technologies has the potential to double the five-year survival rate o= f patients with HNC as well offer the potential to improve care for other c= ancer types as well. |
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February 1, 2021
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O=
rchestration of distributed image archives Recent advances in the use of HTTP range requ= ests to traverse bioformats*, coupled with a general move to =E2=80=9Czero = footprint=E2=80=9D image informatics solutions, enable the creation of imag= e archives as an exercise in governance. A particular feature of this confi= guration is that the images do not have to be copied or moved from their pr= imary location. This has two interesting effects: a) the image owner remain= s in control of its governance, and b) training of AI classifiers can be fe= derated across image sets that are not even shared. * Bremer E, Saltz J, Almeida JS.&= nbsp;ImageBox 2 =E2=80=93 Efficient and rapid access of ima= ge tiles from whole-slide images using serverless HTTP range requests. J Pa= thol Inform 2020;11:29 |
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December 7, 2020 |
NIAID TB Bio=
portal The= NIAID Office of Cyber Infrastructure and Computational Biology will share = their TB Portals Program, including their efforts at imaging data collectio= n, data dissemination, tool development, and data science research.&n= bsp;https://tbportals.ni= aid.nih.gov
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November 16, 2020 | Andrey Fedorov, PhD, Dennis Bontempi |
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November 2, 2020 | MONAI Stephen Aylward, Prerna Dogra, Jorge Cardoso
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October 5, 2020 | Kheops Jo=C3=ABl Spalt= enstein, Osman Ratib |
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September 14, 2020 | Computational Imaging for Precision M= edicine: A quest for generalizable AI models Satish Viswanat= h |
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July 6, 2020 | Distributed Learning of Deep Learning in Medical Imaging= p> Daniel Rubin
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June 1, 2020 | ACR's AI-LAB Laura Coo= mbs, Chris Treml |
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April 6, 2020 | PathPresenter - a web-based digital p=
athology and image viewer |
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January 6, 2020
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October 7, 2019 | MP4 file | |
September 9, 2019 | HistoQC: An Open-Source Quality Contr= ol Tool for Digital Pathology Slides Andrew Janowczyk = RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with= Deep Learning Kenneth Philbrick |
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August 5, 2019 | Advanced Methods in Tissue Cytometry<= /strong> Rupert Ecker Presentation by the 4D Necleome I= maging Working Group David Gr=C3=BCnwald |
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July 1, 2019 |
Joint Session with the CPTAC Special Interest Group
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June 3, 2019 |
PRISM Semant= ic Integration Approach Jonathan Bona |
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May 6, 2019 |
ITCR Update Juli Klemm CPTAC Special Interest Group Justin Kirby Scott Gustafson |
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March 4, 2019 | HTT: high-throughput truthing project= Brandon Gallas |
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January 7, 2019
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November 5, 2018 |
CPTAC Imaging Update and new TCIA-CPTAC Pathology Portal Brenda Fevrier-Sullivan, Ashish Sharma Lawrence Tarbox |
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December 3, 2018 | DICOM SR for LIDC collection= Andrey Federov Kaleidoscope: A Series Projection Visua= lization Tool for Review of DICOM Images for Protected Health Information= strong> William Bennett |
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October 1, 2018 | The SEER Virtual Tissue Repository an= d Pathomics Joel Saltz Changes to the QI= N Funding Mechanism Bob Nordstrom TCIA Update&= nbsp; Christina Vivelo MICCAI Conference Debri= ef Keyvan Farahani TCIA Submission Wizard Kirk Smith |
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September 10, 2018 |
Medici Helpdesk Status Update (and= other projects) Jayashree Kalpathy-Cramer CTAC QIN Bob Nordstrom |
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August 6, 2018 | Imaging Data Commons =E2=80=93 RFI an= d RFP Steve Jett, Todd Pihl |
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June 4, 2018 |
Euro-BioImaging and the Image Data Re= source Jason Swedlow, University of Dundee |
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March 5, 2018 | N/A | |
February 5, 2018 |
Update on the Imaging Data Commons Todd Pihl, Steve Jett Medici Help Desk<= /p> Jayashree Kalpathy-Cramer, Karl Helmer |
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January 8, 2018
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Crowds Cure Cancer =E2=80=93 project = description Justin Kirby, FNLCR |
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December 4, 2017 |
Data Integration and Imaging Informatics Project DI-cubed team TCIA= Data Harmonization Project Amrita Basu Demo of DataScope for TCIA clinical data Ashish Sharma Discussion on TCIA submission recommendations Amrita Basu, John Freymann, Justin Kirby |
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November 6, 2017 |
American College of Radiology =E2=80=93 Data Archive and Res=
earch Tool Kit Laura Coombs / Mike Tilkin (ACR) Ca= ncer Research Data Commons Allen Dearry (NCI) Update on the Transformation of NCI Annotation and Image Markup = (AIM) and DICOM SR Measurement Templates David Clunie |
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September 11, 2017 | Lung Cancer Screening Challenge Keyvan Farahani Scientific Overview: Volumetric Com= puted Tomography (CT) for Precision Analysis of Clinical Trial Results (Vol= -PACT) Lawrence Schwartz |
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August 7, 2017 | N/A | |
June 5, 2017 |
The XNAT imaging informatics p= latform Dan Marcus |
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May 1, 2017 |
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March 6, 2017 |
Workshop = Discussions =E2=80=93 Informatics Needs in Medical Imaging =Bob Nordstrom & Ed Helton, NCI Gene Lightfoot, SAS |
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February 6, 2017 |
Veterans Affairs Precision Oncology P= rogram (POP) and APOLLO activities Luis Selva, VA |
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January 9, 2017
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A Cancer Research Data Ecosystem Warren Kibbe, NCI CBIIT |
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December 5, 2016 |
C= linical Proteomic Tumor Analysis Consortium (CPTAC) Chris Kinsinger |
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October 26, 2016 |
Pathomics B= ased Biomarkers =E2=80=93 Tools and Methods Joel Saltz MD, PhD |
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September 12, 2016 |
LesionTracker: Open-source oncology web viewer Gordon Harris, Dana-Farber/Harvard Cancer Center |
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June 6, 2016 |
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May 2, 2016 |
MAASTRO =E2=80=93 NBIA integration wi= th OpenClinica |
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April 4, 2016 |
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March 7, 2016 |
CTIIP NCIP Imaging Co-clin= ical/animal models Bob Cardiff, UC Davis |
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February 1, 2016 |
CTIIP NCIP Imaging Co-clinical/animal m= odels Bob Cardiff, UC Davis |
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January 4, 2016
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PESSCARA: Platform to Enable Sharing of Sc= ientific Computing Algorithms and Research Panagiotis Korfiatis, PhD, Bradley Erickson, MD PhD |
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December 7, 2015 |
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November 2, 2015 |
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September 14, 2015 |
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July 6, 2015 |
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June 1, 2015 |
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March 2, 2015 |
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February 2, 2015 |
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January 12, 2015
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December 15, 2014 |
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November 3, 2014 |
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October 6, 2014 |
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September 8, 2014 |
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August 11, 2014 |
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April 7, 2014 |
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March 3, 2014 |
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February 3, 2014
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November 4, 2013 |
Imaging Informatics Work Group Project Larry Clarke An= notation and Image Markup 2013 Project Plan and Milestones Pattanasak Mongkolwat, Ph.D. |
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August 5, 2013 |
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June 3, 2013 |
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March 11, 2013
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October 1, 2012 |
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July 2, 2012 |
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