Comments (2)
Thanks for your answers. They are helpful!
from pathomicfusion.
Hi @cuicathy - thank you for your interest in our repository, and raising this issue.
Regarding the first + second points, the discrepancies in c-Index performance can be attributed how we computed overall c-Index using predicted risks per patient (not per data sample). To get predicted risk per patient, the mean accuracy for the 9 patches is computed for each sample, as you suggested. To better reproduce exact numbers, we recently refactored our utils.py script, with the analysis code separated (and expanded) into its own section. Please also see this Jupyter Notebook for evaluation on GBMLGG and KIRC, with the exact function used for computing c-Index here..
Regarding the third point, the evaluation should be performed and ran on images for path-only evaluation, so the 512 x 512 patches should be the inputs (using the PathgraphomicDatasetLoader).
Apologies for the confusion, as evaluation (with many combinations for multimodal fusion for different tasks) is complex.
from pathomicfusion.
Related Issues (20)
- Reproducibility of the GBMLGG results HOT 2
- Regarding reprudicing the same result of pathomic fusion HOT 2
- About the CIndex HOT 1
- Regard reproducing the GBMLGG grade calssification HOT 6
- CPC training HOT 2
- Can't find some .pt files about graph features HOT 5
- CPC_model checkpoints missing HOT 2
- TypeError: scatter_mean() takes from 2 to 5 positional arguments but 6 were given HOT 12
- How does the loss function for grade task work (CNN-only)? HOT 3
- Segmentation fault (core dumped)
- Is the molueculare subtype feature included in the Grade classification task? HOT 4
- The generation of pkl files HOT 2
- Validation data is same as testing data?
- TypeError: scatter_mean() takes from 2 to 5 positional arguments but 6 were given HOT 1
- About the pkl files HOT 3
- options.py HOT 1
- KNN for Cell Graph Construction HOT 1
- could not find MaskCNN and resnet_custom HOT 2
- Data cannot be downloaded HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from pathomicfusion.