The (very) basics of AI for the Radiology resident.
Also on YouTube: https://youtu.be/ia90UKjlmBA
Artificial Intelligence, Machine Learning, Deep Learning, CNN, Convolutional Neural Networks, Support Vector Machine (SVM), GPU. Felipe Kitamura. Pedro Vinícius Staziaki.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Lessons from building a stream-first metadata platform | Shirshanka Das, StealthHostedbyConfluent
"For data-driven enterprises, the most important objective is unlocking the value of their data. To enable this, data scientists are increasingly turning towards data discovery tools (also known as data catalogs) that can help them locate the right dataset or insight and use it correctly. But are all data catalogs the same?
In this talk, I describe how a stream-first architecture was a critical design element that benefited the implementation of our data catalog. We follow the evolution of LinkedIn DataHub’s architecture over the past few years from a simple search tool to a streaming metadata platform that drives productivity and governance workflows across the company.
Join this talk to learn:
* How different data discovery / catalog tools are architected and the tradeoffs in each kind of architecture
* How streaming architectures can benefit metadata
* How event-driven metadata architectures can supercharge your data productivity and governance workflows at your company"
AI in healthcare and Automobile Industry using OpenPOWER/IBM POWER9 systemsGanesan Narayanasamy
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Industires such as Healthcare and Automotive , the AI ladder and AI life cycle and infrastructure architecture considerations.
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at info@uplatz.com
Big data is one of the most popular terms in the IT industry during the past decade. The word is vague and broad enough that essentially every one of us is living in a big-data world. Every time you do a google search, like a post in Facebook, write something in WeChat or view some item on Amazon, you both use and contribute to someone's big data system. Managing so much data across many computers introduce unique challenges. In this talk, we review the landscape of big data platforms and discuss some lessons we learned from building them.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Lessons from building a stream-first metadata platform | Shirshanka Das, StealthHostedbyConfluent
"For data-driven enterprises, the most important objective is unlocking the value of their data. To enable this, data scientists are increasingly turning towards data discovery tools (also known as data catalogs) that can help them locate the right dataset or insight and use it correctly. But are all data catalogs the same?
In this talk, I describe how a stream-first architecture was a critical design element that benefited the implementation of our data catalog. We follow the evolution of LinkedIn DataHub’s architecture over the past few years from a simple search tool to a streaming metadata platform that drives productivity and governance workflows across the company.
Join this talk to learn:
* How different data discovery / catalog tools are architected and the tradeoffs in each kind of architecture
* How streaming architectures can benefit metadata
* How event-driven metadata architectures can supercharge your data productivity and governance workflows at your company"
AI in healthcare and Automobile Industry using OpenPOWER/IBM POWER9 systemsGanesan Narayanasamy
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Industires such as Healthcare and Automotive , the AI ladder and AI life cycle and infrastructure architecture considerations.
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at info@uplatz.com
Big data is one of the most popular terms in the IT industry during the past decade. The word is vague and broad enough that essentially every one of us is living in a big-data world. Every time you do a google search, like a post in Facebook, write something in WeChat or view some item on Amazon, you both use and contribute to someone's big data system. Managing so much data across many computers introduce unique challenges. In this talk, we review the landscape of big data platforms and discuss some lessons we learned from building them.
Maximize the Value of Your Data: Neo4j Graph Data PlatformNeo4j
In this 60-minute conversation with IDC, we will highlight the momentum and reasons why a graph data platform is a breakthrough solution for businesses in need of a flexible data model.
Please join Mohit Sagar, Group Managing Director of CIO Network, as he hosts the conversation with Dr. Christopher Lee Marshall, Associate VP at IDC, and Nik Vora, Vice President of APAC at Neo4. During this very exciting discussion, you'll discover the insights and knowledge unlocked with the graph data platform.
Big Data & Analytics continues to redefine business. Data has transitioned from an underused asset to the lifeblood of the organisation, and a critical component of business intelligence, insight and strategy.
Big Data Scotland is the largest annual data analytics conference held in Scotland: it is supported by ScotlandIS and The Data Lab and free for delegates to attend. The conference is geared towards senior technologists and business leaders and aims to provide a unique forum for knowledge exchange, discussion and cross-pollination.
The programme will explore the evolution of data analytics; looking at key tools and techniques and how these can be applied to deliver practical insight and value. Presentations will span a wide array of topics from Data Wrangling and Visualisation to AI, Chatbots and Industry 4.0.
Key Topics
• Tools and techniques
• Corporate data culture, business processes, digital transformation
• Business intelligence, trends, decision making
• AI, Real-time Analytics, IoT, Industry 4.0, Robotics
• Security, regulation, privacy, consent, anonymization
• Data visualisation, interpretation and communication
• CRM and Personalisation
Real time analytics with Spark Streaming by Padma at Bangalore I & D meetup (https://www.meetup.com/Bengaluru-Insights-and-Data-Meetup/events/238459154)
A Perspective from the intersection Data Science, Mobility, and Mobile DevicesYael Garten
Invited talk at Stanford CSEE392I (Seminar on Trends in Computing and Communications) April 24, 2014.
Covered three topics: (1) Data science at LinkedIn. (2) Mobile data science — how is it different, challenges and opportunities. Examples of how data science impacts business and product decisions. (3) Mobile today, and LinkedIn's mobile story.
The talk presents the evolution of Big-Data systems from single-purpose MapReduce frameworks to fully general computational infrastructures. In particular, I will follow the evolution of Hadoop, and show the benefits and challenges of a new architectural paradigm that decouples the resource management component (YARN) from the specifics of the application frameworks (e.g., MapReduce, Tez, REEF, Giraph, Naiad, Dryad, Spark,...). We argue that beside the primary goals of increasing scalability and programming model flexibility, this transformation dramatically facilitates innovation.
In this context, I will present some of our contributions to the evolution of Hadoop (namely: work-preserving preemption, and predictable resource allocation), and comment on the fascinating experience of working on open- source technologies from within Microsoft. The current Hadoop APIs (HDFS and YARN) provide the cluster equivalent of an OS API. With this as a backdrop, I will present our attempt to create the equivalent of stdlib for the cluster: the REEF project.
Carlo A. Curino received a PhD from Politecnico di Milano, and spent two years as Post Doc Associate at CSAIL MIT leading the relational cloud project. He worked at Yahoo! Research as Research Scientist focusing on mobile/cloud platforms and entity deduplication at scale. Carlo is currently a Senior Scientist at Microsoft in the Cloud and Information Services Lab (CISL) where he is working on big-data platforms and cloud computing.
Bridging the Gap: Analyzing Data in and Below the CloudInside Analysis
The Briefing Room with Dean Abbott and Tableau Software
Live Webcast July 23, 2013
http://www.insideanalysis.com
Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets.
Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.
Maximize the Value of Your Data: Neo4j Graph Data PlatformNeo4j
In this 60-minute conversation with IDC, we will highlight the momentum and reasons why a graph data platform is a breakthrough solution for businesses in need of a flexible data model.
Please join Mohit Sagar, Group Managing Director of CIO Network, as he hosts the conversation with Dr. Christopher Lee Marshall, Associate VP at IDC, and Nik Vora, Vice President of APAC at Neo4. During this very exciting discussion, you'll discover the insights and knowledge unlocked with the graph data platform.
Big Data & Analytics continues to redefine business. Data has transitioned from an underused asset to the lifeblood of the organisation, and a critical component of business intelligence, insight and strategy.
Big Data Scotland is the largest annual data analytics conference held in Scotland: it is supported by ScotlandIS and The Data Lab and free for delegates to attend. The conference is geared towards senior technologists and business leaders and aims to provide a unique forum for knowledge exchange, discussion and cross-pollination.
The programme will explore the evolution of data analytics; looking at key tools and techniques and how these can be applied to deliver practical insight and value. Presentations will span a wide array of topics from Data Wrangling and Visualisation to AI, Chatbots and Industry 4.0.
Key Topics
• Tools and techniques
• Corporate data culture, business processes, digital transformation
• Business intelligence, trends, decision making
• AI, Real-time Analytics, IoT, Industry 4.0, Robotics
• Security, regulation, privacy, consent, anonymization
• Data visualisation, interpretation and communication
• CRM and Personalisation
Real time analytics with Spark Streaming by Padma at Bangalore I & D meetup (https://www.meetup.com/Bengaluru-Insights-and-Data-Meetup/events/238459154)
A Perspective from the intersection Data Science, Mobility, and Mobile DevicesYael Garten
Invited talk at Stanford CSEE392I (Seminar on Trends in Computing and Communications) April 24, 2014.
Covered three topics: (1) Data science at LinkedIn. (2) Mobile data science — how is it different, challenges and opportunities. Examples of how data science impacts business and product decisions. (3) Mobile today, and LinkedIn's mobile story.
The talk presents the evolution of Big-Data systems from single-purpose MapReduce frameworks to fully general computational infrastructures. In particular, I will follow the evolution of Hadoop, and show the benefits and challenges of a new architectural paradigm that decouples the resource management component (YARN) from the specifics of the application frameworks (e.g., MapReduce, Tez, REEF, Giraph, Naiad, Dryad, Spark,...). We argue that beside the primary goals of increasing scalability and programming model flexibility, this transformation dramatically facilitates innovation.
In this context, I will present some of our contributions to the evolution of Hadoop (namely: work-preserving preemption, and predictable resource allocation), and comment on the fascinating experience of working on open- source technologies from within Microsoft. The current Hadoop APIs (HDFS and YARN) provide the cluster equivalent of an OS API. With this as a backdrop, I will present our attempt to create the equivalent of stdlib for the cluster: the REEF project.
Carlo A. Curino received a PhD from Politecnico di Milano, and spent two years as Post Doc Associate at CSAIL MIT leading the relational cloud project. He worked at Yahoo! Research as Research Scientist focusing on mobile/cloud platforms and entity deduplication at scale. Carlo is currently a Senior Scientist at Microsoft in the Cloud and Information Services Lab (CISL) where he is working on big-data platforms and cloud computing.
Bridging the Gap: Analyzing Data in and Below the CloudInside Analysis
The Briefing Room with Dean Abbott and Tableau Software
Live Webcast July 23, 2013
http://www.insideanalysis.com
Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets.
Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.
We've been taught that "data science" is the esoteric domain of PhDs,
but like anything else, it's easy once you understand it. This talk
explains the basics of data science, covering concepts in supervised
learning (including a detailed explanation of decision trees and
random forests) as well as examples of unsupervised learning
algorithms. Far from being a dry and academic topic, data science and machine learning are useful and practical analytical tools. (This talk is intended for a general audience.)
Topics will include:
1) An introduction to supervised learning using the popular decision
tree algorithm
2) The concepts of training and scoring, and the meaning of "real time"
machine learning
3) Model validation using holdout sets
4) Model complexity and overfitting; understanding bias and variance;
using ensembles to reduce variance
5) An overview of unsupervised learning models including clustering,
topic modeling and anomaly detection
and more!
Data Con LA 2022 - Real world consumer segmentationData Con LA
Jaysen Gillespie, Head of Analytics and Data Science at RTB House
1. Shopkick has over 30M downloads, but the userbase is very heterogeneous. Anecdotal evidence indicated a wide variety of users for whom the app holds long-term appeal.
2. Marketing and other teams challenged Analytics to get beyond basic summary statistics and develop a holistic segmentation of the userbase.
3. Shopkick's data science team used SQL and python to gather data, clean data, and then perform a data-driven segmentation using a k-means algorithm.
4. Interpreting the results is more work -- and more fun -- than running the algo itself. We'll discuss how we transform from ""segment 1"", ""segment 2"", etc. to something that non-analytics users (Marketing, Operations, etc.) could actually benefit from.
5. So what? How did team across Shopkick change their approach given what Analytics had discovered.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Slides for the afternoon session on "Introduction to Bioinformatics", delivered at the James Hutton Institute, 29th, 20th May and 5th June 2014, by Leighton Pritchard and Peter Cock.
Slides cover introductory guidance and links to resources, theory and use of BLAST tools, and a workshop featuring some common tools and tasks.
Deep Learning in Recommender Systems - RecSys Summer School 2017Balázs Hidasi
This is the presentation accompanying my tutorial about deep learning methods in the recommender systems domain. The tutorial consists of a brief general overview of deep learning and the introduction of the four most prominent research direction of DL in recsys as of 2017. Presented during RecSys Summer School 2017 in Bolzano, Italy.
Developing in R - the contextual Multi-Armed Bandit editionRobin van Emden
Attached, the slides of my presentation on how to create R packages, illustrated with lessons learned in developing "contextual": a package that enables you to easily simulate and analyze contextual multi-armed bandit algorithms.
Code: https://github.com/Nth-iteration-labs/contextual
This was part of a webinar from the Materials Research Society on Machine Learning, AI, and Data-Driven Materials Development and Design. The spoken content (including Q&A) is available through MRS.
Similar to The (very) basics of AI for the Radiology resident (20)
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
The (very) basics of AI for the Radiology resident
1. The (very) basics of AI for
the Radiology resident
May 25, 2021
Pedro V. Staziaki MD1, Felipe Kitamura, MD2
1. PGY-5 Radiology, Department of Radiology, Boston Medical Center, Boston University School of Medicine
2. Head of Innovation in Diagnostic Operations, Dasa
3. Basic resources on Coursera
• DeepLearning.AI (Andrew Ng)
• AI For Everyone à Very short and basic course
• AI For Medicine Specialization
• AI For Medical Diagnosis à Intermediate, some coding
• Stanford University (Matthew Lungren)
• Fundamentals of Machine Learning for Healthcare
8. Why radiologists need to adapt to a future with AI
1. Learn the basics of AI for use in clinical practice
2. Evaluate AI algorithms for use in clinical practice
3. Contribute to AI use case development
4. Curate and annotate for AI training and validation
5. Run, adapt and create AI models for local use
6. Collaborate with other institutions to improve models
7. Develop AI models for commercial use
Allen B et al. JACR. 2019.
9. Why radiologists need to adapt to a future with AI
•There is a hunger for
• Radiologists who want to get involved with AI
• Domain-experts in Radiology
• Someone who can say what is clinically relevant
• Peer-reviewers for AI research
10. Why radiologists need to adapt to a future with AI
•Radiologists can
• Understand limitations and errors
• Collaborate with multidisciplinary team
• Critical appraisal of the literature
• Evaluate AI products
• Move Radiology forward
12. What is AI?
•Confusing terms
•Artificial Intelligence ?
•Machine Learning ?
•Deep Learning ?
•Neural Networks ?
•Big Data ?
•Data Science ?
13. What is AI?
AI
Machine learning
Deep Learning
• Artificial Intelligence
• A technique which
enables machines to
mimic human behavior
• Machine learning
• Subset using statistical
methods
• Deep learning
• Subset making
computation with multi-
layer neural networks
14. What is AI?
•Big Data → there is an excess of data!
Amount
of
data
Time
15. What is AI?
•Big Data → ↑ in data, 6 Vs
•↑ volume
•↑ variety
•↑ velocity
•↑ veracity
•↑ value
•↑ variability
21. What is AI?
•Data Science
• Data analytics
• Data mining
• Data security
• Data management
• Data visualization
• Statistical analysis
22. What is AI?
•Data Science
• Examine the data and provide insights
• Optimize sales
• Find correlations
•Machine learning
• Make predictions
• Classify things automatically
23. What is AI?
AI
Machine learning
Deep Learning
• Artificial Intelligence
• A technique which
enables machines to
mimic human behavior
• Machine learning
• Subset using statistical
methods
• Deep learning
• Subset making
computation with multi-
layer neural networks
25. What is AI?
•Applications of AI
•Improve work-flow and work life
•Improve quality and safety
•Decrease health care errors
•Accelerate medical discoveries
27. • ML is the study of computer algorithms that improve
automatically with experience and data
• Developed based a training data set
• Makes predictions without being explicitly programmed to do so
• Supervised learning
• Labeled data: groups “normal” and “pneumonia”
• Unsupervised learning
• Automatically finds groups “normal” and “pneumonia”
What is machine learning?
Erickson BJ et al., RadioGraphics, 2017
28. What is machine learning?
• Examples
• Support Vector Machine (SVM) → Supervised Learning
• Decision trees → Supervised Learning
• Deep Learning (CNN) → Supervised Learning
• Clustering → Unsupervised Learning
30. What is Support Vector Machine (SVM)
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* Optimal hyperplane
*
31. What is Support Vector Machine (SVM)
• A type of supervised machine learning
• Input data → map into a parameter space
• Using a plane (called a support vector)
• Looking for a greater separation
32. What is Support Vector Machine (SVM)
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Which line separates better?
33. What is Support Vector Machine (SVM)
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This one!
34. What is Support Vector Machine (SVM)
✕
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✕
✕
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✕
✕
This one!
How to make
model find the
BEST line?
35. What is Support Vector Machine (SVM)
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Train model to find two parallel lines that
are as far away as possible
36. What is Support Vector Machine (SVM)
• Step 1: Start with a random line, and two equidistant parallel to it
• Step 2: Pick a large number of repetitions, the epochs (1000)
• Step 3: Pick a number close to 1, the expanding factor (0.99)
• Step 4: Repeat 1000 times
• Pick random point
• If correct → do nothing; If incorrect → move line towards the point
• Separate the lines using the expanding factor
• Step 5: Boom! You have the line that best separates the data
37. What is Support Vector Machine (SVM)
• New functions that can map points to other dimensions with
nonlinear relationships → Classify examples not linearly separable
40. What is a decision tree?
• A type of supervised machine learning
• Produce human-readable rules for classification
• Intuitive, explainable
• Searches the many possible combinations of decision points to
find the points that will result in the simplest tree with the most
accurate results
• But does not generalize well in the real world
41. What is a decision tree?
• Give an input
• Looks for which feature has the most right and wrong answers
• Stops splitting when, e.g., < 10 samples
42. What is a decision tree?
• Ensemble methods
• Boosting and random forest
• Improve accuracy
• More than one decision tree is constructed
43. What is a decision tree?
• Boosted forest
• Multiple decision trees by repeatedly resampling the training data by
means of replacement, and voting on the trees to reach a consensus
prediction
• All features are considered
• Random forest → No data resampling
• Only a subset of the total number of features is randomly selected
• Only uses the best split feature from the subset
• More generalizable
50. What is Deep Learning?
Nodes ~ cell body Links ~ axons
Neural network
51. What is Deep Learning?
Features
4, 5
Label
1
Try to output as close as
possible to an output of 1
52. What is Deep Learning?
Features
4, 5
Label
1
0.2
What I got
(probability)
53. What is Deep Learning?
Features
4, 5
Label
1
0.2
What I got
(probability)
What I wanted
(label)
1
54. What is Deep Learning?
Features
4, 5
Label
1
0.2
What I got
(probability)
What I wanted
(label)
1
Task
↑ 0.2
Hey, NN, you task is to increase
the 0.2 when I give you the
features 4 and 5 as input
55. What is Deep Learning?
Features
4, 5
Label
1
0.2
What I got
(probability)
What I wanted
(label)
1
Task
↑ 0.2
Gradient
ascent
↑ ln(0.2)
56. What is Deep Learning?
Features
4, 5
Label
1
0.2
What I got
(probability)
What I wanted
(label)
1
Task
↑ 0.2 ↑ ln(0.2)
W → W + (learning rate)(derivative)
Gradient
ascent
57. What is Deep Learning?
• A type of supervised machine learning
• Multi-layered neural networks (aka deep neural networks)
• Deep → many layers (> 20), often > 100
• Does not require image feature identification as a first step
• Features are identified as part of the learning process
• Needs all the computing power from video game GPUs
• NVidia Corporation
Erickson BJ et al., RadioGraphics, 2017
58. What is Deep Learning?
• Uses
Soffer S et al., Radiology, 2019
Classification
Detection
Segmentation
59. What is Deep Learning?
• Convolutional neural networks (CNN)
• Main subtype of NN
• Assumes that inputs have a geometric relationship
• Like the rows and columns of pixels
• Input layer → convolution of a small image
• Convolutions → filters extract edges or boundaries in matrices
Erickson BJ et al., RadioGraphics, 2017
61. Soffer S et al., Radiology, 2019
What is Deep Learning?
62.
63. What is Deep Learning?
• Layers of a CNN
1. Convolution layer
• Boundaries, edges
• Shape
• Size
2. Activation layer → ReLU (rectified linear unit)
3. Pooling layer
4. Fully connected layer
Erickson BJ et al., RadioGraphics, 2017
64. Soffer S et al., Radiology, 2019
What is Deep Learning?
1
2
3 4
65. What is Deep Learning?
• Architectures → combinations of layers and layer sizes
• LeNet
• ResNet34
• U-Net
• VGG
• LSTM
• Inception
Soffer S et al., Radiology, 2019
66. What is Deep Learning?
Soffer S et al., Radiology, 2019
AlexNet
67. What is Deep Learning?
Soffer S et al., Radiology, 2019
VGG
77. Small sample sizes
• Leave-one-out (LOO) cross validation
• Extreme case
• Remove just one example for testing
• Use all the others for each round of training
• Then classify that example
• Repeat until each sample has been left out
79. Workflow of a machine learning project
• Before anything
• Clinically relevant question
• Just like any research
• Define cohort well
• Collect data
• Clean data (90% of time spent)
• Garbage in, garbage out
80. Workflow of a machine learning project
• Simply put
• Collect data
• Train model
• Deploy model
81. Workflow of a machine learning project
• Simply put
• Collect data
• Train model
• Deploy model
1. Clinically relevant question
2. Data cohort definition
3. Dataset acquisition
4. Data cleaning and normalization
5. De-identification
6. Dataset annotation
7. Data partitioning
8. Model building
9. Model evaluation
10. Clinical analysis, interpretation
84. Limitations and challenges of AI
• Patient privacy
• Data access
• Lack of standard acquisition protocols
• Lack of uniformity
• Artifacts
• Bias in AI
87. Relevant references
1. DeepLearning.AI courses: AI For Everyone and AI For Medical
Imaging, by Andrew Ng
2. Artificial Intelligence: Nuts & Bolts of Machine Learning for
Medical Imaging, by Katherine P. Andriole, at the NERRS 2021
3. Allen, Bibb et al. Democratizing AI. Journal of the American
College of Radiology, 2019
4. Erickson, Bradley J et al. Machine Learning for Medical Imaging.
RadioGraphics, 2017
5. Soffer, Shelly et al. Convolutional Neural Networks for
Radiologic Images: A Radiologist’s Guide. Radiology, 2019