++ +
+_Oncology FM Evaluation Framework by kaiko.ai_ [](https://pypi.python.org/pypi/kaiko-eva) -[](https://kaiko-ai.github.io/eva/latest) -[](https://github.com/kaiko-ai/eva#license) +[](https://kaiko-ai.github.io/eva/latest) +[](https://github.com/kaiko-ai/eva#license)
+[](https://openreview.net/forum?id=FNBQOPj18N¬eId=FNBQOPj18N)
Installation ā¢
@@ -59,12 +63,12 @@ eva --version
## How To Use
-_eva_ can be used directly from the terminal as a CLI tool as follows:
+_`eva`_ can be used directly from the terminal as a CLI tool as follows:
```sh
eva {fit,predict,predict_fit} --config url/or/path/to/the/config.yaml
```
-When used as a CLI tool, `_eva_` supports configuration files (`.yaml`) as an argument to define its functionality.
+When used as a CLI tool, _`eva`_ supports configuration files (`.yaml`) as an argument to define its functionality.
Native supported configs can be found at the [configs](https://github.com/kaiko-ai/eva/tree/main/configs) directory
of the repo. Apart from cloning the repo, you can download the latest config folder as `.zip` from your browser from
[here](https://download-directory.github.io/?url=https://github.com/kaiko-ai/eva/tree/main/configs). Alternatively,
@@ -98,7 +102,7 @@ and [tutorials](https://kaiko-ai.github.io/eva/dev/user-guide/advanced/replicate
## Benchmarks
-In this section you will find model benchmarks which were generated with `_eva_`.
+In this section you will find model benchmarks which were generated with _`eva`_.
### Table I: WSI patch-level benchmark
@@ -129,15 +133,15 @@ over 5 runs, with an average standard deviation of Ā±0.003._
_References_:
-1. _"Emerging properties in self-supervised vision transformersā_
-2. _"Benchmarking self-supervised learning on diverse pathology datasetsā_
-3. _"Scaling self-supervised learning for histopathology with masked image modelingā_
-4. _"A General-Purpose Self-Supervised Model for Computational Pathologyā_
-5. _"Towards Training Large-Scale Pathology Foundation Models: from TCGA to Hospital Scaleā_
+1. _"Emerging properties in self-supervised vision transformersā_, [arXiv](https://arxiv.org/abs/2104.14294)
+2. _"Benchmarking self-supervised learning on diverse pathology datasetsā_, [arXiv](https://arxiv.org/abs/2212.04690)
+3. _"Scaling self-supervised learning for histopathology with masked image modelingā_, [medRxiv](https://www.medrxiv.org/content/10.1101/2023.07.21.23292757v1)
+4. _"A General-Purpose Self-Supervised Model for Computational Pathologyā_, [arXiv](https://arxiv.org/abs/2308.15474)
+5. _"Towards Training Large-Scale Pathology Foundation Models: from TCGA to Hospital Scaleā_, [arXiv](https://arxiv.org/pdf/2404.15217)
## Contributing
-_eva_ is an open source project and welcomes contributions of all kinds. Please checkout the [developer](./docs/DEVELOPER_GUIDE.md)
+_`eva`_ is an open source project and welcomes contributions of all kinds. Please checkout the [developer](./docs/DEVELOPER_GUIDE.md)
and [contributing guide](./docs/CONTRIBUTING.md) for help on how to do so.
All contributors must follow the [code of conduct](./docs/CODE_OF_CONDUCT.md).
@@ -162,7 +166,23 @@ Our codebase is built using multiple opensource contributions
+