Add folds 0, 2, 3 and 4 of the HCM model
Browse filesAdds the other four folds of the five-fold cross-validation (hcm/fold_{0,2,3,4}/checkpoint_final.pth). They use the same plans.json and dataset.json as fold 1. The model card now describes all five folds, and its examples use the five-fold ensemble.
- README.md +8 -4
- hcm/fold_0/checkpoint_final.pth +3 -0
- hcm/fold_2/checkpoint_final.pth +3 -0
- hcm/fold_3/checkpoint_final.pth +3 -0
- hcm/fold_4/checkpoint_final.pth +3 -0
README.md
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@@ -19,7 +19,7 @@ the `Segment` step of [OpenHeart](https://github.com/ImperialCollegeLondon/OpenH
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| Framework | nnU-Net v2, `nnUNetTrainer`, `2d` configuration |
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| Folds |
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| Input | one short-axis cine volume (NIfTI), one channel |
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| Training data | 1,299 labelled short-axis cine volumes (end-diastole and end-systole) from patients with hypertrophic cardiomyopathy at 5 centres |
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@@ -30,16 +30,20 @@ from huggingface_hub import snapshot_download
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import openheart as oh
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model = snapshot_download("ImperialCollegeLondon/OpenHeart") + "/hcm"
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case = oh.Segment(model
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```
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```bash
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MODEL=$(python -c 'from huggingface_hub import snapshot_download as d; print(d("ImperialCollegeLondon/OpenHeart"))')/hcm
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openheart run cases/ -o out/ --model "$MODEL"
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```
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`hcm/` is a standard nnU-Net results folder, so it also loads directly with
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`nnUNetPredictor.initialize_from_trained_model_folder(model, use_folds=(1,))`.
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## Limitations
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|---|---|
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| Framework | nnU-Net v2, `nnUNetTrainer`, `2d` configuration |
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| Folds | all five folds of a five-fold cross-validation (`hcm/fold_0` … `hcm/fold_4`), each trained on 1,039–1,040 volumes and validated on the other 259–260 |
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| Input | one short-axis cine volume (NIfTI), one channel |
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| Training data | 1,299 labelled short-axis cine volumes (end-diastole and end-systole) from patients with hypertrophic cardiomyopathy at 5 centres |
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import openheart as oh
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model = snapshot_download("ImperialCollegeLondon/OpenHeart") + "/hcm"
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case = oh.Segment(model)(oh.Case.load("cases/subject1", layout="legacy"))
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```
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```bash
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MODEL=$(python -c 'from huggingface_hub import snapshot_download as d; print(d("ImperialCollegeLondon/OpenHeart"))')/hcm
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openheart run cases/ -o out/ --model "$MODEL"
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```
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Without `folds`, all five folds are used and their predictions averaged. `folds=[1]`
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(`--folds 1`) runs a single fold, which is about five times faster. To download one fold
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only, pass `allow_patterns=["hcm/*.json", "hcm/fold_1/*"]` to `snapshot_download`.
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`hcm/` is a standard nnU-Net results folder, so it also loads directly with
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`nnUNetPredictor.initialize_from_trained_model_folder(model, use_folds=(0, 1, 2, 3, 4))`.
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## Limitations
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hcm/fold_0/checkpoint_final.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:968f8a30b3e9f3c2453470d2ccefb90322f9cb548971f52f59156061d780ec4c
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size 165573758
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hcm/fold_2/checkpoint_final.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:8dadfff5ae28d3639512e783486225de0c3fa661a7567bcd37cf61c622c6f136
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size 165574014
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hcm/fold_3/checkpoint_final.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:e6508dcf86af9cfe8ebc2f0a011af6f774f3a3d3dfe34910db45db801ad767fc
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size 165572798
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hcm/fold_4/checkpoint_final.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:8cf62b825e43340dcc3f82cb2f61c0057951054fc77610468e448f4614fcb23b
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size 165573822
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