CHIEF
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Harvard Medical School,Massachusetts Institute of Technology (MIT),Sichuan University,Sun Yat-sen University,Shenzhen Maternity & Child Healthcare Hospital,Chongqing University Cancer Hospital,Harvard University,University of Pennsylvania,Cedars-Sinai Medical Center,Broad Institute,Dana-Farber Cancer Institute,Brigham and Women's Hospital,Tencent,Massachusettes General Hospital,Pennsylvania State University,Jinan University,Stanford University
- Organisation type
- Academia,Academia,Academia,Academia,Academia,Academia,Research collective,Industry,Academia,Academia,Academia
- Country
- United States of America, China
- Published
- 4 September 2024
- Authors
- Xiyue Wang, Junhan Zhao, Eliana Marostica, Wei Yuan, Jietian Jin, Jiayu Zhang, Ruijiang Li, Hongping Tang, Kanran Wang, Yu Li, Fang Wang, Yulong Peng, Junyou Zhu, Jing Zhang, Christopher R. Jackson, Jun Zhang, Deborah Dillon, Nancy U. Lin, Lynette Sholl, Thomas Denize, David Meredith, Keith L. Ligon, Sabina Signoretti, Shuji Ogino, Jeffrey A. Golden, MacLean P. Nasrallah, Xiao Han, Sen Yang, Kun-H…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology, Vision, Medicine
- Task
- Cancer diagnosis, Image classification, Object detection
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
(44 TB × 10^12 bytes) ÷ 60,530 WSIs = 7.26 × 10^8 bytes/WSI (256 × 256 × 3) = 196,608 bytes per tile (7.26 × 10^8 bytes) ÷ (2 × 10^5 bytes/tile) = 3,630 tiles/WSI 60,530 WSIs × 3,630 tiles/WSI = 2.19 × 10^8 tiles 1.5 × 10^7 + 2.19 × 10^8 = 2.34 × 10^8 total datapoints Final estimate: 2.25 × 10^8 datapoints
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (non-commercial)
- Training code
- Open (non-commercial)
AGPL-3.0 license https://github.com/hms-dbmi/CHIEF "CHIEF is made available under the GPLv3 License and is available for non-commercial academic purposes." "Downloading Pre-trained models Request access to the model weights. "
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- A Pathology Foundation Model for Cancer Diagnosis and Prognosis Prediction
- Last updated
- 28 November 2025
What the numbers mean
What this model is
CHIEF was published by Harvard Medical School,Massachusetts Institute of Technology (MIT),Sichuan University,Sun Yat-sen University,Shenzhen Maternity & Child Healthcare Hospital,Chongqing University Cancer Hospital,Harvard University,University of Pennsylvania,Cedars-Sinai Medical Center,Broad Institute,Dana-Farber Cancer Institute,Brigham and Women's Hospital,Tencent,Massachusettes General Hospital,Pennsylvania State University,Jinan University,Stanford University, in United States of America, in September 2024. It comes out of academia,Academia,Academia,Academia,Academia,Academia,Research collective,Industry,Academia,Academia,Academia.
It works in Biology, Vision, Medicine, and is recorded as doing cancer diagnosis, Image classification, Object detection.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
CHIEF — common questions
Who created CHIEF?
CHIEF was published by Harvard Medical School,Massachusetts Institute of Technology (MIT),Sichuan University,Sun Yat-sen University,Shenzhen Maternity & Child Healthcare Hospital,Chongqing University Cancer Hospital,Harvard University,University of Pennsylvania,Cedars-Sinai Medical Center,Broad Institute,Dana-Farber Cancer Institute,Brigham and Women's Hospital,Tencent,Massachusettes General Hospital,Pennsylvania State University,Jinan University,Stanford University, based in United States of America, categorised as academia,Academia,Academia,Academia,Academia,Academia,Research collective,Industry,Academia,Academia,Academia.
When was CHIEF released?
CHIEF was published in September 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is CHIEF used for?
CHIEF works in Biology, Vision, Medicine, and is recorded as handling cancer diagnosis, Image classification, Object detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download CHIEF?
The weights for CHIEF are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run CHIEF?
We cannot say. CHIEF has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is CHIEF open source?
Its weights are published, so CHIEF can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does CHIEF have?
No parameter count has been published for CHIEF, which is why no memory or speed figure appears on this page.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.