Deep Learning Affinity Group (DLAG)

Deep Learning Affinity Group (DLAG)

Fred Hutch Logo

Journal Club

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, September 16, 2025
    12:10-1:00 p.m.
    Hybrid: Arnold Building M3-A805 and Zoom
    Presenter/Discussion lead: TBD
    Topic: TBD
  • 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

  • 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. 
    Fundraise
    Quick Links