Upload 7 files
Browse files- README.md +59 -0
- config.json +13 -0
- model.onnx +3 -0
- model.safetensors +3 -0
- slide_ids_test.txt +201 -0
- slide_ids_train.txt +810 -0
- torchscript_model.pt +3 -0
README.md
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---
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license: cc-by-4.0
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---
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---
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license: cc-by-4.0
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---
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# Survival prediction using PORPOISE (TCGA GBMLGG)
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This model predicts a patient's overall survival using an H&E-stained digital pathology image of GBMLGG. It was trained by Jakub Kaczmarzyk using PORPOISE. It is an attempt to reproduce the PORPOISE manuscript.
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Original journal article: https://doi.org/10.1016/j.ccell.2022.07.004
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If you find this model useful, please make sure you cite the original publication.
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Inputs: Bag of patches with 128um edge length, embedded with CTransPath.
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Output classes: logits of hazards at four timepoints
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To calculate the arbitrary risk score given the model outputs `logits`, use the following:
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```python
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hazards = torch.sigmoid(logits)
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S = torch.cumprod(1 - hazards, dim=1)
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risk = -torch.sum(S, dim=1)
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```
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## Data
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TCGA-GBMLGG was used to train the model. The whole slide images were tiled into 128x128um patches, and each patch was encoded using CTransPath (this produces 768-dimensional embeddings).
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The training and validation splits were provided by the original PORPOISE code. Here, we report the model in fold 3, because it had the highest c-index of the folds.
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Samples sizes:
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- Train: 810 slides (455 patients)
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- Validation: 201 slides (114 patients)
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## Reusing this model
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To use this model on the command line, see [WSInfer-MIL](https://github.com/kaczmarj/wsinfer-mil).
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Alternatively, you may use PyTorch on ONNX to run the model. First, embed 128um x 128um patches using CTransPath. Then pass the bag of embeddings to the model.
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```python
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import onnxruntime as ort
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import numpy as np
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embedding = np.ones((1_000, 768), dtype="float32")
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ort_sess = ort.InferenceSession("model.onnx")
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logits, attention = ort_sess.run(["logits", "attention"], {'input': embedding})
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# To get the risk score, implement the following:
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# hazards = sigmoid(logits)
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# S = cumprod(1 - hazards, dim=1)
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# risk = -sum(S, dim=1)
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```
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The median risk score was -3.22, and this value was used to split patients into low risk and high risk.
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## Model performance
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The model achieves a c-index of 0.83 in the validation set.
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# Intended uses
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This model is ONLY intended for research purposes.
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**This model may not be used for clinical purposes.** This model is distributed without warranties, either express or implied.
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config.json
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{
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"spec_version": "1.0",
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"type": "porpoise",
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"patch_size_um": 128,
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"feature_extractor": "ctranspath",
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"num_classes": 4,
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"class_names": [
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"logits-time0",
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"logits-time1",
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"logits-time2",
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"logits-time3"
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]
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}
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model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:1c1eb0d39b61b557264adc04dc38755a761eab9813862e1038b3c3472134261b
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size 2637665
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:980ce2dee88a626b571820bb7274cfc53fcfb3b436ccad42f726171a3e3d9cc6
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size 2635724
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slide_ids_test.txt
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TCGA-R8-A6MK-01Z-00-DXA.088A6643-8205-45A9-AF02-8C039D9F653E
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TCGA-R8-A6MK-01Z-00-DXB.F02C56E8-9793-4272-B14E-031A06EE2C8F
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TCGA-R8-A6MK-01Z-00-DXC.F741DE76-6E81-4DCF-A99F-659C1E398FB0
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TCGA-R8-A6MK-01Z-00-DXD.26CA7DE4-FEBC-4544-829F-163A7FB04DA7
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TCGA-R8-A6MK-01Z-00-DXE.7888C0C3-818C-4C36-9EC6-F708B257872A
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TCGA-VM-A8CH-01Z-00-DX3.B0A4A9A2-14B8-4080-8DF8-B2055EFFFDF8
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TCGA-VM-A8CH-01Z-00-DX4.E0A57F2D-B9E0-4423-8920-8A99CFEC4CE9
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TCGA-VM-A8CH-01Z-00-DX5.992B2EBF-4B11-441E-97DE-9167BDBF797D
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TCGA-VM-A8CH-01Z-00-DX6.284D2734-21E1-4C09-9578-6BFE42131D43
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TCGA-VM-A8CH-01Z-00-DX7.8A5184E2-AEAF-4665-A09C-044D2B21EDD5
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TCGA-WY-A85D-01Z-00-DX1.FB8C252B-7A88-4B14-B3C9-217E3EA76008
|
slide_ids_train.txt
ADDED
|
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| 1 |
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TCGA-02-0047-01Z-00-DX1.4755D138-5842-4159-848C-4248D6D53DE0
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| 2 |
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TCGA-06-0129-01Z-00-DX1.b7bddf7d-f39e-45e7-a78d-319d3a4d2dae
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| 5 |
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| 9 |
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| 10 |
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| 20 |
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| 21 |
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| 23 |
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| 24 |
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TCGA-06-0210-01Z-00-DX5.ac28952b-2394-4477-a9eb-276b0d6e9d67
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| 25 |
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TCGA-06-0219-01Z-00-DX1.592a9605-e9ec-46f5-a055-5001088f47d0
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| 26 |
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TCGA-06-0219-01Z-00-DX2.530ad4a9-eed1-4127-8dd3-b0a7a6cb2b7d
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| 27 |
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TCGA-06-0644-01Z-00-DX1.bbce7e5c-6df3-4963-ac75-9f566a2fde12
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| 28 |
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TCGA-06-0645-01Z-00-DX1.ffca480d-519c-4628-844c-946b5d000d01
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| 29 |
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TCGA-06-0646-01Z-00-DX1.459f1e60-f299-4fea-bc00-9aa068e67f76
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| 30 |
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TCGA-06-0646-01Z-00-DX2.81b40946-c2b9-4850-9f96-da44593561a5
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| 31 |
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| 32 |
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TCGA-06-0686-01Z-00-DX1.fc0b2e05-94e0-4540-b519-6fc0c81e8d6f
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TCGA-VM-A8CE-01Z-00-DX6.E5A828E7-0B84-4EE6-9337-99608E269886
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TCGA-VM-A8CE-01Z-00-DX7.016DD6CE-4B9B-45A1-B94C-27C11EBF5575
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TCGA-VM-A8CE-01Z-00-DX8.94355113-F8C2-401E-988C-B3112B054186
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TCGA-VM-A8CE-01Z-00-DX9.31516257-7E21-4797-ACCE-084FB67D82AA
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| 790 |
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TCGA-VM-A8CF-01Z-00-DX1.B02A927B-2E2F-4255-9949-E45813703A7C
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| 791 |
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TCGA-VM-A8CF-01Z-00-DX2.9BB80EF8-5600-4BA5-879D-46B8B6D0776D
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| 792 |
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TCGA-VM-A8CF-01Z-00-DX3.3CED09BD-4062-4573-A075-84461B82F5B4
|
| 793 |
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TCGA-VM-A8CF-01Z-00-DX4.AF3112EB-D194-48BD-B31F-5138814578A3
|
| 794 |
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TCGA-VM-A8CF-01Z-00-DX5.90CAA7E9-55BA-41D6-A5D1-280E761B17BC
|
| 795 |
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TCGA-VM-A8CF-01Z-00-DX6.DC99FC24-7290-4702-B5DC-45F3780757B6
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| 796 |
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TCGA-VM-A8CF-01Z-00-DX7.91F19B77-0929-4068-BFFB-FBA10E4F01DB
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| 797 |
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TCGA-VM-A8CF-01Z-00-DX8.4E93F16B-3CC3-4094-9EF2-9B74351AAF95
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| 798 |
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TCGA-VM-A8CF-01Z-00-DX9.704D20F4-2DDD-42FB-8AD6-97C2BCA68C83
|
| 799 |
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TCGA-VV-A829-01Z-00-DX1.C5464B6B-E673-4AFE-A5A1-7923F2C40450
|
| 800 |
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TCGA-VV-A829-01Z-00-DX2.4E2AB800-C97E-4293-886B-28793FFBAB47
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| 801 |
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TCGA-VV-A829-01Z-00-DX3.5ACEDBA9-4972-4E9B-A5BA-4D0CFA28B8CC
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| 802 |
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TCGA-VV-A86M-01Z-00-DX1.3D542AC0-4000-40AA-A574-791B8772F956
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| 803 |
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TCGA-W9-A837-01Z-00-DX1.F0E23873-46D1-460F-AA53-930D7934E687
|
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TCGA-WH-A86K-01Z-00-DX1.6F32811A-D54A-4716-8364-758DC98D9E47
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| 805 |
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TCGA-WY-A858-01Z-00-DX1.4D36E628-9C20-46D9-8617-C6903A1535A9
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TCGA-WY-A859-01Z-00-DX1.650758FF-1BE8-4A56-8A9A-319BA0363D8F
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TCGA-WY-A85A-01Z-00-DX1.CB302B89-F89A-40FD-A7DE-6E12BEFBA2AE
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| 808 |
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TCGA-WY-A85B-01Z-00-DX1.1E4B796A-A1E3-45F9-8075-61DE8FBF0B80
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| 809 |
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TCGA-WY-A85C-01Z-00-DX1.E0A6429A-91B3-4FFE-9FF9-28D956864D24
|
| 810 |
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TCGA-WY-A85E-01Z-00-DX1.AA7A4C1F-99AA-490D-B6D4-280EAB1EFF56
|
torchscript_model.pt
ADDED
|
@@ -0,0 +1,3 @@
|
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|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:f4168c1584035d3958293072402e77caae58f9e09a3f79673f1f6ff9af476a0c
|
| 3 |
+
size 2655355
|