Nick Steele is Semifinalist at Stanford, Connor Engel in top 32 — The  Harvard-Westlake Debate Team CS372 Artificial Intelligence for Disease Diagnosis                                a 

CS372 Artificial Intelligence for Disease Diagnosis        

          and Information Recommendations

 

 

Spring 2021 T Th 8:30am – 9:50am (Zoom), from March 30th to June 3rd

 

3 units/20 sessions, including lectures and project discussion/presentation.

Online zoom link, registration required

 

Edward Chang (echang@cs.stanford.edu)

Adjunct Professor, Computer Science, Stanford University

AI/NLP Advisor, SmartNews

President, DeepQ Healthcare (2012-2021)

Director of Research, Google (2006-2012)

 

TA: Saahil Jain (saahil.jain@stanford.edu)

 

 

Announcements

 

§ (5/27) Lecture #18 slides and video online (only lecture made public). 

§ (5/25) Lecture #17 slides and video online. 

§ (5/18) Lecture #15 slides and video online

§ (5/13) Lecture #14 slides and video online. 

§ (5/8) Lecture #12 slides and video online. 

§ (5/4) Lecture #11 slides and video online. 

§ (4/29) Lecture #10 slides and video online. 

§ (4/22) Lecture #8 slides and video online.  Project proposal due @11:59pm today.

§ (4/20) Lecture #7 by Rich online. 

§ (4/15) Lecture #6 slides and video online.  Project proposal due in a week on 4/22.

§ (4/13) Lecture #5 slides and video online.

§ (4/8) Lecture #4 slides and video online. 

§ (4/6) Lecture #3 slides and video online.  Please join Lecture #4 in real time to brainstorm project topics. 

§ (4/5) Assignment #1 posted, due at 10am on April 13th.

§ (4/1) Lecture #2 slides and video online (video can be assessed only by Stanford’s students.)

§ (3/30) Lecture #1 slides and video online (video can be assessed only by Stanford’s students.)

§ (3/29) Three guest speakers Dr. Melissa Ko (Cancer Biologist, Stanford), Dr. Vikas Duvvuri (PHD/MD, Stanford), and Rich Jaroslovsky (CJO of SmartNews) will cover subjects of cancer, depression/anxiety disorder, and information polarization.

§ (3/28) Received several audit requests.  Audit is not permitted as we would like all to involve in hands-on problem solving.

 

About

 

Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks.  However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago.  This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, subspace learning for enabling interpretability, privacy-preserving data management, and other topics.  (The 2021 edition plans to cover quantum ML models.)  

 

The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment) will feature guest speakers from academia and industry.  The information recommendation part of this course in 2021 will address the problem of global political polarization.  Students will be assigned to work on a term project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas.  Students are welcome to formulate a project that leverages their own graduate research. 

 

Level: Senior/graduate of EE/CS, Medicine, Medical Informatics, Engineering; proficient in programming

Perquisite: Introductory course in AI, Statistics, or Machine Learning

 

Time and Location

 

·  Time: Tuesdays and Thursdays 8:30am to 9:50am, starting on March 30th.

·  Location: On Zoom (Link is provided at Stanford AXESS.  Enrolled students only.)

 

Assignments

 

·  No exams.

·  Three written assignments in the first half of the quarter.

·  Reading:  A list of papers/articles/podcasts grouped by topics.  Each student selects one topic of interest to conduct in-depth surveys and presentation.

·  Term project: A group project of two to three involving algorithm design and coding.  Some past example projects are:

§     COVID-19 demographic analysis

§     COVID-19 outbreak tracking and prediction

§     Thoracic disease diagnosis with limited X-ray images

§     News article summarization (extractive and abstractive)

§     Methods for fusing knowledge and perception (text and image)

 

Grading

 

·  Assignments 30%

·  Literature survey and project proposal 20%

·  Project implementation and demo 50%

 

  Syllabus

 

Date

Description

Course Materials

Notes

Week #1

3/30/2021

Course Aims and Syllabus

[Video][Slides]

 

Deep Learning Review [link], Afshine Amidi and Shervine Amidi (Stanford CS230)

 

 

Prof. Edward Chang

4/1/2021

P4 Medicine Part I:

Healthcare Paradigm Shift

[Video][Slides]

[1] Edward Y. Chang, et al., "Artificial Intelligence in XPRIZE DeepQ Tricorder. " ACM MM Workshop for Personal Health and Health Care, 2017.

[2] FC Chang, JJ Chang, CN Chou, EY Chang, “Toward Fusing Domain Knowledge with Generative Adversarial Networks to Improve Supervised Learning for Medical Diagnoses”, FC Chang, JJ Chang, CN Chou, EY Chang, IEEE MIPR Conference, 2019.

[3] Edward Y. Chang, et al., "Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning." AAAI, 2018.

[4] Transfer representation learning for medical image analysis, Chuen-Kai Shie, Chung-Hisang Chuang, Chun-Nan Chou, Meng-Hsi Wu, Edward Y Chang, IEEE EMBC, 2015.

 

Prof. Edward Chang

 

 

Week #2

4/6

P4 Medicine Part II:

w/ small data and Inductive Bias

[Video][Slides]

[1] Schwartz, W. B., R. S. Patil, and P. Szolovits. "AI in Medicine: where do we stand." New England Journal of Medicine 316 (1987): 685-688. [link].

[2] Wu, T. D. "Efficient Diagnosis of Multiple Disorders Based on a Symptom Clustering Approach." Proceedings of AAAI, 1990, pp. 357-364.

[3] Universal Equivariant Multilayer Perceptrons, Siamak Ravanbakhsh, ICML, 2020.

[4] The problem of Protein Folding, Wikipedia.

[5] On Bottleneck of Graph Neural Networks and its Practical Implications, Uri Alon, Eran Yahav, ICRL 2021.

[6] SIGN: Scalable Inception Graph Neural Networks, Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, Federico Monti, GraphSaint, 2020.

[7] Panel: VR/AR for Surgery and Medical Education, April 2018 [link].

[8] Stanford SNI Talk: Advancing Healthcare w/ AI and VR, Edward Y. Chang [link] (Stanford ID required).

[9] Daphne Koller:  Biomedicine and Machine Learning, AI Podcast #93 with Lex Fridman, May 2020 [link]

 

Prof. Edward Chang

 

Assignment #1:

Deadline 4/13

4/8

NLP Part I:

Hand-engineering, Similarity, Collaborative Filtering, Latent Dirichlet Allocation [Video][Slides]

 

Brainstorming on term projects, datasets and resources

 

[1] Attention is all you need, Ashish Vaswani, et al., [link].

[2] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [link].

[3] Reformer: The Efficient Transformer [link].

[4] Perceiver: General Perception with Iterative Attention [link].

                 

Prof. Edward Chang

 

 

Week #3

4/13

NLP Part II:

Attention, Transformers, BERT, and GPT [Video][Slides]

 

[1] Attention is all you need, Ashish Vaswani, et al., [link].

[2] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [link].

[3] Reformer: The Efficient Transformer [link].

[4] Perceiver: General Perception with Iterative Attention [link].

 

Prof. Edward Chang

 

Project Proposal

Deadline 4/22

4/15

NLP Part III:

CLI: Conversational Language Interface, Dialogue & Virtual Assistant (Alexa, Google Home, & Oval), and Conversational Language Interface

[Video][Slides]

 

[1] GPT3 and multimodal pre-trained models

[2] OVAL

[3] Chirpy Cardinal

[4] Genie

 

Prof. Edward Chang

 

 

Week #4

4/20

Information Recommendations: The Crisis of Global News and Information Polarization

[video][Slides]

 

Project signed up, project group formed, and project abstract due

[1] Can a Divided America Heal?  Ted Talk [link], Jonathan Haidt, 2016

[2] … more will be provided by Rich.

 

 

 

Each abstract includes 1) key objective, 2) related work,

3) proposed methods, and 4) data sources.

 

Rich Jaroslovsky, VP/Chief Journalist, SmartNews

 

 

4/20

Proposal Discussion (by appointment)

Session #1 10am – 11:30am

Session #2 2pm – 3:30pm

Each group can sign up for a 30-min slot.  Please sign up @ Slack.

Saahil Jain

4/22

Cancer Causes and Diagnosis

[video][Slides]

[1] Principles and methods of integrative genomic analyses in cancer, Vessela N. Kristensen, Ole Christian Lingjærde, Hege G. Russnes, Hans Kristian M. Vollan, et al., Nature Reviews, 2014.

[2] Biomarker development in the precision medicine era: lung cancer as a case study, Ashley J. Vargas, and Curtis C. Harris, Natural Reviews, 2016.

[optional] Artificial intelligence in radiology, Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz,and Hugo J. W. L. Aerts, Nature Reviews, 2018.

 

Dr. Melissa Ko,

PhD, Cancer Biology,

Stanford

 

Project Proposal due         

@ 11:59pm

 

Week #5

4/27

Project Proposal Presentation, title, goals, methods, and datasets

 

 

[1] Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019). From recognition to cognition: Visual commonsense reasoning. In Proceedings of the IEEE Conference on Computer Vision

and Pattern Recognition, pp. 6720–6731.

[2] Bahri, Y., Kadmon, J., Pennington, J., Schoenholz, S. S., Sohl-Dickstein, J., & Ganguli, S. (2020). Statistical mechanics of deep learning. Annual Review of Condensed Matter Physics.

[3] Perceiver: General Perception with Iterative Attention [link].

[4] Stanford OVAL COVID-19 Appointment & QA app [link]

 

Prof. Edward Chang

4/29

Cancer Treatment and

How AI May Help

[video][Slides]

[1] Mass cytometry: blessed with the curse of dimensionality, Evan W Newell & Yang Cheng, Nature Immunology, June 2016.

[2] Next-Generation Machine Learning for Biological Networks, Diogo M. Camacho, Katherine M. Collins, Rani K. Powers, James C. Costello, and James J. Collins, Leading Edge Review, June 2018.

[optional] Personalized Cancer Models for Target, Discovery and Precision Medicine, Carla Grandori1 and Christopher J. Kemp, Trends in Cancer, CellPress Reviews, September 2018.

 

Dr. Melissa Ko,

PhD, Cancer Biology,

Stanford

 

 

Week #6

5/4

Intell. & Consciousness Part I:

History of AI in Diagnosis and its Promises and Limitations

[video][Slides]

 

 

 

[1] Edward Y. Chang et al., "Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning." AAAI (2018).

[2] Edward Y. Chang et al., "REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis." NIPS (2018).

[3] Szolovits, P., and S. G. Pauker. "Categorical and Probabilistic Reasoning in Medical Diagnosis.” Artificial Intelligence 11(1-2), 1978: 115-144.

[4] Wu, T. D. "Efficient Diagnosis of Multiple Disorders Based on a Symptom Clustering Approach." Proceedings of AAAI, 1990, pp. 357-364.

[5] Patil, R. S., P. Szolovits, and W. B. Schwartz. "Causal Understanding of Patient Illness in Medical Diagnosis." In Proceedings of the Seventh International Joint Conference on Artificial Intelligence.

 

Prof. Edward Chang

 

Assignment #3

Deadline 5/18

5/6

AI for Psychiatry: Beyond Brainless and Mindless?

[video][Slides]

[1] Students Face Mental Health Challenges --- barriers to care (during COVID-19)”, Stanford Daily (4/30/2020) [link].

[2] Meditation Music: Sangchhen Dorji Lhuendrup Nunnery, Bhutan, Himalayas, Edward Y. Chang [link].

 

Dr. Vikas Duvvuri,

MD/PhD Stanford

 

 

Week #7

5/11

Project Check-in: methods, datasets, experiments, and preliminary results

 

 

Prof. Edward Chang

5/13

Intell. & Consciousness Part II:

What and Where is Consciousness?

Rule of Nature, Principles of Similarity and Classification.

[video][Slides]

 

[1] What is Life, Schrodinger, Cambridge University Press, 1992

[2] Discovery of Discovery of a Perceptual Distance Function for Measuring Image Similarity Beitao Li, Edward Chang, and Yi Wu, Multim. Syst. 8(6)512-522 () [link]

 

Prof. Edward Chang

Week #8

5/18

An Uncertain Future: Coping with COVID-19 anxiety with facts and right perspectives

[video][Slides]

 

 

This pandemic is not going away soon.  It has impact health, economy, and living tremendously, perhaps the most drastic one in our lifetime this far.  How should we deal with it mentally?  Dr. Duvvuri lectures on COVID-19 and AI.

 

Dr. Vikas Duvvuri,

MD/PhD Stanford

5/20

AI in Healthcare Panel

[video]

Open discussion on various topics including AI, healthcare, and career advice.

Moderator: Saahil Jain

Panelists:

Vikas Duvvuri, 

Pranav Rajpurkar,

Edward Chang

 

Week #9

5/25

Data Privacy and Regulations Compliance           

[video][Slides]

 

[1] GDPR, CCPA and HIPAA.

[2] Differential Privacy

[3] Soteria: A Provably Compliant User Right Manager Using a Novel Two-Layer Blockchain Technology, Stanford University, DeepQ, NTU, Edward Y. Chang el al., 2020 [link].

 

Prof. Edward Chang

5/27

Intell. & Consciousness Part III:

AI Future: Consciousness, Mind, and Ethics [video] [slides]

 

1] What is Life, Erwin Schrödinger, 1944

[2] The Emperor’s New Mind, Roger Penrose, 2016 edition

[3] Divine Comedy, Dante Alighieri, 1320

 

Prof. Edward Chang

6/1: 8:30am – 10:20am

Project: final presentations

Session #1

 

Final report due on 6/4

6/3: 8:30am – 10:20pm

Project: final presentations

Session #2

Final report due on 6/4