Time: 04/28 Wed 3pm-4pm
Remote Access
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Meeting ID: 956 1719 7636 Passcode: 924293
Title: Brain imaging genetics for Alzheimer's disease: integrated analysis and machine learning
Li Shen, Ph.D.
Professor of Informatics
Department of Biostatistics, Epidemiology and Informatics
Perelman School of Medicine
University of Pennsylvania
Bio: Li Shen, Ph.D., is a Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University of Pennsylvania. He is an elected fellow of the American Institute for Medical and Biological Engineering (AIMBE). He obtained his Ph.D. degree in Computer Science from Dartmouth College. The central theme of his lab is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high throughput omics data, cognitive and other biomarker data, electronic health record (EHR) data, and rich biological knowledge such as pathways and networks, with applications to complex disorders. His research interests include medical image computing, biomedical informatics, machine learning, network science, imaging genomics, Alzheimer's disease, and big data science in biomedicine. He has authored over 280 peer-reviewed articles (h-index 57) in these fields. Dr. Shen's work has been continuously supported by the NIH and NSF, and he is presently the PI of multiple NIH and NSF grants on developing computational methods for various biomedical applications including brain imaging genomics, genetics of Alzheimer's disease, genetics of human connectome, mining drug effects from the EHR data, and big data mining in brain science. He is co-leading the NIA Alzheimer's Disease Sequencing Project AI4AD Consortium and oversees the imaging genomics aspect of this landmark project. Dr. Shen served as the Executive Director of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Board of Directors during 2016-2019. He has chaired and co-chaired various professional meetings in medical image computing and bioinformatics. He is an Associate Editor of BioData Mining and Frontiers in Radiology (Section of AI in Radiology), and serves on the Editorial Board of Medical Image Analysis and Brain Imaging and Behavior.
Abstract: Brain imaging genetics is an emerging data science field, where integrated analysis of brain imaging and genetics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the genetic, molecular and phenotypic characteristics of the brain as well as their impact on normal and disordered brain function and behavior. Many methodological advances in brain imaging genetics are attributed to large-scale landmark biobank projects such as the Alzheimer's Disease Sequencing Project, the Alzheimer's Disease Neuroimaging Initiative, and the UK Biobank. Using the study of Alzheimer's disease as an example, we will discuss fundamental concepts, state-of-the-art statistical and machine learning methods, and innovative applications in this rapidly evolving field. We show that the wide availability of brain imaging genetics data from various large-scale biobanks, coupled with advances in biomedical statistics, informatics and computing, provides enormous opportunities to contribute significantly to biomedical discoveries in brain science and to impact the development of new diagnostic, therapeutic and preventative approaches for complex brain disorders such as Alzheimer's disease.
More details:
https://bmi.
Visual Analytics and Machine Learning for Biomedical Imaging Diagnosis
Arie Kaufman
We present an integrated approach using visual analytics and machine learning (ML) to diagnose abnormalities in 3D radiological imaging and biological microscopes. The primary example will involve 3D virtual pancreatography (VP), a novel visualization-ML procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes an ML-based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, an ML-based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists. Other applications include virtual colonoscopy, COVID-19, pathology, brain neurites, etc.
Biography: Arie Kaufman is Distinguished Professor and formerChair of the Department of Computer Science at Stony Brook University, where he is also Director of the Center for Visual Computing (CVC), and Chief Scientist at the Center of Excellence in Wireless and Information Technology (CEWIT).
He received his PhD in Computer Science at Ben-Gurion University of the Negev in 1977. He is known for his work in visualization, graphics, virtual reality, user interfaces, multimedia, and their applications, especially in bio-medicine. He is especially well known for his work on the 3-dimensional virtual colonoscopy, a revolutionary low-risk technique for colon cancer screening, and for pioneering the use of Graphics Processing Units (GPUs) and GPU-clusters. In 2012, he presided over the development and opening of the Reality Deck, the largest virtual reality display in the world, at Stony Brook University.
Kaufman was the founding Editor in Chief of IEEE Transactions on Visualization and Computer Graphics (TVCG), co-founded the IEEE Visualization Conference and Volume Graphics series, and is currently the director of IEEE Computer Society Technical Committee on Visualization and Graphics. He is an IEEE Fellow, ACM Fellow, winner of many awards, including the IEEE Visualization Career Award, and member of the European Academy of Sciences.
Steven Skiena is inviting you to a scheduled Zoom meeting.
Topic: AI Seminar: Arie Kaufman
Time: Apr 21, 2021 10:00 AM Eastern Time (US and Canada)
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All are welcome to attend BMI grand rounds talk by Dr. Le Lu on 04/14.
Le Lu, Ph.D
Executive Director, PAII Inc
Johns Hopkins University
IEEE Fellow, MICCAI Board Member
Time: Wednesday, April 14, 2021 3:00 pm - 4:00 pm
Zoom Meeting
https://stonybrook.zoom.us/j/
Meeting ID: 956 1719 7636 Passcode: 924293
Title:
In Search of Effective and Reproducible Clinical Imaging Biomarkers for Population Health and Oncology Applications of Screening, Diagnosis and Prognosis
Bio:
Le Lu received a PhD in 2007 from Johns Hopkins University. During his first six years at Siemens, he made significant contributions to the company's CT colonography and Lung CAD product lines. From 2013 to 2017, Dr. Lu served as a staff scientist in the Radiology and Imaging Sciences department of the National Institutes of Health Clinical Center. He then went on to found Nvidia's medical image analysis group and he held the position of senior research manager until June 2018. Since then, he has been the Executive Director at PAII Inc., Bethesda Research lab, Maryland, USA which has become one of the leading industrial research labs in medical imaging. He was the main technical leader for two of the most-impactful public radiology image dataset releases (NIH ChestXray14, NIH DeepLesion 2018). He won NIH Clinical Center Director Award in 2017, NIH Mentor of the year award in 2015, and won numerous best paper awards in MICCAI and RSNA from 2016 to 2020 (over 10000 citations). In 2021, He was elected into IEEE Fellow class cited for his contribution to machine learning for cancer detection and diagnosis, and MICCAI society board member (MICCAI-Industry Workgroup Chair). He is currently an Associate Editor for IEEE Trans. Pattern Analysis and Machine Intelligence and IEEE Signal Processing Letters. He has served as an Area Chair for recent MICCAI, AAAI, CVPR, WACV, ICIP and ICHI conferences for 14 times.
Abstract:
This talk will first give an overall on the work of employing deep learning to permit novel clinical workflows in two population health tasks, namely using conventional ultrasound for liver steatosis screening and quantitative reporting; osteoporosis screening via conventional X-ray imaging and AI readers. These two tasks were generally considered as infeasible tasks for human readers, but as proved by our scientific and clinical studies and peer-reviewed publications, they are suitable for AI readers. AI can be a supplementary and useful tool to assist physicians for cheaper and more convenient/precision patient management. Next, the main part of this talk describes a roadmap on three key problems in pancreatic cancer imaging solution: early screening, precision differential diagnosis, and deep prognosis on patient survival prediction. (1) Based on a new self- learning framework, we train the pancreatic ductal adenocarcinoma (PDAC) segmentation model using a larger quantity of patients (≈1,000, four institutions), with a mix of annotated/unannotated venous or multi-phase CT images. Pseudo annotations are generated by combining two teacher models with different PDAC segmentation specialties on unannotated images, and can be further refined by a teaching assistant model that identifies associated vessels around the pancreas. Our approach makes it technically feasible for robust large-scale PDAC screening from multi-institutional multi-phase partially-annotated CT scans. (2) We propose a holistic segmentation-mesh classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask. Our results are comparable to a multimodality clinical test that combines clinical, imaging, and molecular testing for clinical management of patients with cysts. (3) Accurate preoperative prognosis of resectable PDACs for personalized treatment is highly desired in clinical practice. We present a novel deep neural network for the survival prediction of resectable PDAC patients, 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE- ConvLSTM), to derive the tumor attenuation signatures from CE-CT imaging studies. Our framework can significantly improve the prediction performances upon existing state-of-the-art survival analysis methods. This deep tumor signature has evidently added values (as a predictive biomarker) to be combined with the existing clinical staging system.
More information can be found at:
https://bmi.
Dates:
Friday, April 16, 2021 - 2:40pm to 3:40pm
Location:
Zoom - contact events@cs.stonybrook.edu for Zoom info.
Dates:
Friday, April 2, 2021 - 4:00pm to 5:00pm
Location:
Zoom - contact events@cs.stonybrook.edu for Zoom info.
Please join us for another installment of Discover IACS as we introduce the research group of Professor Heather Lynch! In the absence of in-person gatherings, we hope this will serve as an opportunity to network and synergize.
We hope you will tune in!
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Meeting ID: 933 8484 9113
Passcode: 443047
Dial by your location
Topic: AI Seminar: Owen Rambow
Time: Mar 17, 2021 10:00 AM Eastern Time (US and Canada)
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Meeting ID: 936 1464 4178. Passcode: 965936
Natural Language Understanding and Semantic Parsing
(Partly joint work with former colleagues at Elemental Cognition)
Semantic parsing refers to the task of determining the propositional content of language: who did what to whom. It is part of the larger task of natural language understanding (NLU). I will start out by discussing what full NLU means, and argue that we are still far away, as a field, from solving full NLU, or even from knowing how to evaluate it.
In the second part of the talk, I will situate semantic parsing in the context of several other NLU subtasks. Typically, the target representation of semantic parsing uses an ontology (such as PropBank or FrameNet). Semantic parsing includes the subtasks of word sense disambiguation, argument detection, and argument role labeling. I will discuss choices among possible target ontologies. I will justify why we created a new ontology, Hector, based on FrameNet and the lexical resource NOAD, and explain some of its characteristics.
In the third part of the talk, I will present experiments we performed using transformer models. We obtain best results using a two-phase model, in which we first choose the frame, and then, given the frame, choose the arguments. We encode the problem for both tasks using indices in the sentence. While we develop the parser for our new ontology Hector, this approach also beats the state of the art for FrameNet and PropBank parsing.Biography: I am a professor in the Department of Linguistics at Stony Brook University with a joint appointment in IACS.
Until recently, I was a research scientist at Elemental Cognition. Elemental Cognition is working on deep natural language understanding.
I got my PhD with Aravind Joshi at the University of Pennsylvania in 1994. I have worked at CoGenTex, and at AT&T Labs -- Research, and for many years I was a research scientist at Columbia University in the Center for Computational Learning Systems.
Dates:
Wednesday, March 3, 2021 - 6:00pm to 7:30pm
Location:
Zoom - contact events@cs.stonybrook.edu for Zoom info.
Event Description:
Women in Computer Science (WiCS), the Society of Women Engineers (SWE), and the Stony Brook Robotics Team (SBRT) are collaborating to host an event called Inspiring Women in STEM Academia: A Community Dialogue to address the lack of female representation in STEM academia.
All are invited to attend so they may gain a better understanding of the challenges faced by their female colleagues and hear perspectives on how they can offer support in the workplace. Given the shockingly disproportionate number of female professionals in STEM academia, we feel that this event would be extremely beneficial for male faculty to listen to and amplify their voices.
It will begin with a discussion panel consisting of Stony Brook professors and faculty who will provide valuable insight into the issue. From there, we will split into smaller discussion groups where student and faculty attendees will be able to voice their opinions, hear about the thoughts/experiences of others, and participate in an engaging discussion with panelists.
The event will be held on March 3rd from 6:00 - 7:30 PM on Zoom.
The following Stony Brook faculty will be panelists:
Dr. Aruna Balasubramanian - Computer Science Professor, WiCS Advisor, WPhD Advisor
Dr. Xinwei Mao - Civil Engineering Assistant Professor
Urszula Zalewski - Director of Experiential Learning, Career Center Advisor (Healthcare)
Dr. Heather Lynch - Ecology and Evolution Professor, Lynch Lab for Quantitative Ecology
Karen Kernan - URECA Director, Simons Summer Research Program Director
Dr. Eszter Boros - Chemistry Assistant Professor, Boros Lab
Dr. Maria Nagan - Chemistry Lecturer, Nagan Research Lab
Topic: Exotic Neural Networks
Time: Mar 10, 2021 08:00 PM Eastern Time (US and Canada)
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Meeting ID: 913 8893 8500
Passcode: 501725