diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..ec4a626 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.pth filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 41ea71f..6de1188 100644 --- a/README.md +++ b/README.md @@ -13,8 +13,8 @@ Here we present the training codes and the pretrained models from each of our ex To train the networks, we used Ubuntu 14.04, CUDA 8.0 and CuDNN v.6. Below please find the other dependencies which need to be installed: -* Python 3 -* pytorch with CUDA support +* Python 3.6 +* pytorch < 0.4.0 with CUDA support * PIL * matplotlib * Jupyter Notebook (to work with attention maps) @@ -31,12 +31,12 @@ However, you can run the codes as they are, just use the parameters fixed in the ## Attention maps examples Our model learns localized radiological findings as we imposed prior anatomical knowledge to teh network architecture. Here are some examples of attention maps and predictions (Kellgren-Lawrence grade 2 ground truth): - + -## TODO +## What is in here - [x] Codes for the main experiements (Supplementary information of the article) -- [ ] Pre-trained models +- [x] Pre-trained models - [ ] Datasets generation scripts - [ ] MOST and OAI cohorts bounding box annotations @@ -44,4 +44,3 @@ Our model learns localized radiological findings as we imposed prior anatomical ## License This code is freely available only for research purpuses. - diff --git a/snapshots_knee_grading/.DS_Store b/snapshots_knee_grading/.DS_Store new file mode 100644 index 0000000..4ad0f6c Binary files /dev/null and b/snapshots_knee_grading/.DS_Store differ diff --git a/snapshots_knee_grading/2017_10_10_12_30_42/args.pkl b/snapshots_knee_grading/2017_10_10_12_30_42/args.pkl new file mode 100644 index 0000000..58bf6de Binary files /dev/null and b/snapshots_knee_grading/2017_10_10_12_30_42/args.pkl differ diff --git 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