|
| 1 | +Name: CHAMMI-75 |
| 2 | +Description: | |
| 3 | + Quantifying cell morphology using images and machine learning models has proven to be a powerful tool to study the response of cells to treatments. |
| 4 | + However, the models used to quantify cellular morphology are typically trained with a single microscopy imaging type and under controlled experimental conditions. |
| 5 | + This results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels), |
| 6 | + or because the target experimental conditions are out of distribution. We have created CHAMMI-75, a large-scale dataset containing 2.8 million multi-channel, |
| 7 | + high-resolution images curated from 75 diverse, publicly available biological studies. This dataset is useful to investigate and develop channel-adaptive models, |
| 8 | + which could process microscopy images of varying technical specifications and regardless of the number of channels. By breaking the limitations of existing models, |
| 9 | + CHAMMI-75 is an invaluable resource for creating the next generation of foundation models for image-based biological research. |
| 10 | +Documentation: https://github.com/CaicedoLab/CHAMMI-75 |
| 11 | +Contact: Juan Caicedo, [email protected] |
| 12 | +ManagedBy: Morgridge Institute for Research |
| 13 | +UpdateFrequency: Every 2 years |
| 14 | +Tags: |
| 15 | + - microscopy |
| 16 | + - machine learning |
| 17 | + - biology |
| 18 | + - life sciences |
| 19 | + - imaging |
| 20 | + - high-throughput imaging |
| 21 | + - cell imaging |
| 22 | + - fluorescence imaging |
| 23 | + - aws-pds |
| 24 | +License: CC BY 4.0 License |
| 25 | +Citation: |
| 26 | +Resources: |
| 27 | + - Description: "CHAMMI-75 Dataset: Images, training set and evaluation set available in an S3 bucket" |
| 28 | + ARN: arn:aws:s3:::chammi-data |
| 29 | + Region: us-west-2 |
| 30 | + Type: S3 Bucket |
| 31 | +DataAtWork: |
| 32 | + Tutorials: |
| 33 | + - Title: "Get To Know A Dataset: CHAMMI-75" |
| 34 | + URL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/ |
| 35 | + NotebookURL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/get-to-know-a-dataset-template.ipynb |
| 36 | + AuthorName: Vidit Agrawal, Juan Caicedo |
| 37 | + - Title: Running CHAMMI-75 Evaluation Benchmarks |
| 38 | + URL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/ |
| 39 | + NotebookURL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/running-benchmarks.ipynb |
| 40 | + AuthorName: Vidit Agrawal, Juan Caicedo |
| 41 | + Tools & Applications: |
| 42 | + - Title: CHAMMI-75 Source Code |
| 43 | + URL: https://github.com/CaicedoLab/CHAMMI-75 |
| 44 | + AuthorName: Vidit Agrawal |
| 45 | + - Title: CHAMMI Benchmarking Source Code |
| 46 | + URL: https://github.com/chaudatascience/channel_adaptive_models |
| 47 | + AuthorName: Chau Pham |
| 48 | + Publications: |
| 49 | + - Title: "CHAMMI: A benchmark for channel-adaptive models in microscopy imaging" |
| 50 | + URL: https://neurips.cc/virtual/2023/poster/73620 |
| 51 | + AuthorName: Zitong Sam Chen, Chau Pham, Siqi Wang, Michael Doron, Nikita Moshkov, Bryan Plummer, Juan C. Caicedo |
| 52 | +ADXCategories: |
| 53 | + - Healthcare & Life Sciences Data |
0 commit comments