The group will host a monthly journal club (usually at noon on the third Tuesday of the month), where we provide an opportunity for faculty to present mini lectures, for trainees to present works in progress, or just to discuss new literature. If you are interested in presenting, please email dlag@fredhutch.org.
Upcoming Schedule
- Tuesday, October 21, 2025
12:10-1:00 p.m.
Hybrid: Arnold Building M3-A805 and Zoom
Presenter/Discussion lead: TBD
Topic: TBD
- Tuesday, November 18, 2025
12:10-1:00 p.m.
Hybrid: Arnold Building M3-A805 and Zoom
Presenter/Discussion lead: TBD
Topic: TBD
- Tuesday, December 16, 2025
12:10-1:00 p.m.
Hybrid: Arnold Building M3-A805 and Zoom
Presenter/Discussion lead: TBD
Topic: TBD
Past Meetings
- June 17, 2025
Recording Available, Slide Deck
Presenter/Discussion lead: Youyi Fong and Gary Zhao
Topic: genAI in Computer Vision: An Introduction to GAN Models and Style Transfer
We will discuss generative adversarial networks (GANs) and style transfer models, with an emphasis on their structure, evolution, and relevance to computer vision tasks.
- March 18, 2025
Recording Available
Presenter/Discussion lead: Ty Lambert (DaSL); Robert McDermott (IT)
Topic: DeepSeek Discussion, continued:
Ty Lambert, AI and Research Data Protections Program Manager, Office of Chief Data Officer
Title: Artificial Intelligence Governance at Fred Hutch
Robert McDermott, Director, Solutions, Engineering & Architecture
Title: Inside the Private Thoughts of AI: How DeepSeek’s Inner Monologue Redefines What We Expect from Language Models
- February 18, 2025
Recording Available; Slide Deck
Presenter/Discussion lead: Eardi Lila and Youyi Fong
Topic: DeepSeek R1 and V3 Tech Reports
Intro, Scaling Laws, - Youyi Fong (VIDD & PHS)
Multitoken Latent Attention, MoE - Eardi Lila (UW Biostat)
- November 19, 2024
Recording Available
Presenter/Discussion lead: Lucas Liu
In this meeting, we will discuss the latest findings from the American Medical Informatics Association (AMIA) Annual Symposium hosted last week. AMIA Annual Symposium is the world's premier meeting for the research and practice of biomedical and health informatic. We will focus on the expert opinion about the current state and future of AI in medical informatics, with special emphasis on the role of NLP/LLMs. In addition, we will discuss a couple of example papers.
Xie Q, Chen Q, Chen A, et al. Me LLaMA: Foundation Large Language Models for Medical Applications, https://arxiv.org/abs/2402.12749
- October 15, 2024
Recording Available
Presenter/Discussion lead: Wei Sun
Topic: Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging Data
In the September journal club, we talked about medical imaging, focusing on CT or MRI images. This month, we will talk about pathology images. Particularly, we will discuss an on-going work that exploits spatial transcriptomic data to annotate pathology images and uses such annotations to train deep learning models to characterize whole slide H&E stained images.
Zhining Sui, Ziyi Li, Wei Sun, bioRxiv 2024.08.05.606654; doi: https://doi.org/10.1101/2024.08.05.606654
- September 17, 2024
Recording Available
Presenter/Discussion lead: Saishi Cui
Transformers in Medical Imaging: A New Era of AI for Diagnosis. This presentation explores the emerging role of transformer models in medical imaging, challenging the dominance of convolutional neural networks (CNNs). By capturing global context, transformers have demonstrated superior performance in a variety of tasks such as image segmentation, classification, and more. This review will cover architectural advancements, their application in diagnostic accuracy, and how transformers are reshaping the future of AI in healthcare. Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023). Transformers in medical imaging: A survey. Medical Image Analysis, 88, 102802.
- June 4, 2024
Recording Available
Presenter/Discussion lead: Si Liu
One Version of Attention in Graph Neural Network
Brody et al. 2022, How attentive are graph attention networks? ICLR, 2022 , which is an updated version of the graph attention as in a previous paper: Velickovic et al. Graph attention networks. ICLR, 2018
- May 28, 2024
Recording Available
Presenter/discussion lead: Wei Sun
A continuation our discussion of the Transformer model that powers chatGPT. We will discuss transformer and related methods for omics data.
TULIP—a Transformer based Unsupervised Language model for Interacting Peptides and T-cell receptors that generalizes to unseen epitopes.
scGPT: toward building a foundation model for single-cell multi-omics using generative AI.
- April 02, 2024
Recording Available Slide Deck
Discussion Lead: Lucas Liu
A continuation of our discussion of the Transformer model that powers GPT and walth through the math behind it step-by-step. Additionally, we will introduce Vision Transformers — an essential breackthrough in image analysis.
Attention is All You Need
An Image is worth 16x16 Words: Transformers for image recognition at scale
- March 19, 2024
Recording Available
Lucas Liu and John Kang: Transformer Model
Discussion of the Transformer model that powers GPT and walth through the math behind it step-by-step. Additionally, we will introduce Vision Transformers — an essential breackthrough in image analysis.
Attention is All You Need
An Image is worth 16x16 Words: Transformers for image recognition at scale
- February 20, 2024
Recording Available
Robert McDermott: Multimodal (language and vision) model
Visual Instruction Tuning
Improved Baselines with Visual Instruction Tuning
Experimentation results and real example
- January 23, 2024
Recording Available
Youyi Fong
Segment Anything Model
A Foundation Model for Cell Segmentation
Fast SAM
- November 21, 2023
Recording Available
Lucas Liu: Survival analysis and deep learning
https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-018-0482-1
https://arxiv.org/abs/1907.00825
https://ojs.aaai.org/index.php/AAAI/article/view/11842
https://www.mdpi.com/2072-6694/14/23/5807
John Kang: Scaling Clinical Trial Matching
https://arxiv.org/abs/2308.02180
- October 17, 2023
Recording Available
Wei Sun
A brief introduction biologically informed neural network and generalizability of neural network methods
John Kang and Youyi Fong
Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography