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 +52 -0
- slide_ids_train.txt +232 -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 KIRP)
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This model predicts a patient's overall survival using an H&E-stained digital pathology image of KIRP. 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-KIRP 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 1, because it had the highest c-index of the folds.
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Samples sizes:
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- Train: 232 slides (208 patients)
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- Validation: 52 slides (52 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 -2.84, 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 0.81 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 sha256:176b4c5a51d0f6467cf3458a4dd55b925c100124a8eae71ef2b393bfeb603724
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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 sha256:4d41f6c1129ba66eba49ea9d26d6881d366dd8140048b3e3ffec894ddbf16c7e
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size 2635724
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slide_ids_test.txt
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TCGA-2Z-A9JE-01Z-00-DX1.B2538A85-D0EA-4C50-BD07-1C59DD8208F6
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TCGA-2Z-A9JN-01Z-00-DX1.714E8577-5BAE-4D99-92DF-1B75500D7AED
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TCGA-5P-A9JV-01Z-00-DX1.E9472303-DC58-4CE5-8A91-CB9275BE0891
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TCGA-5P-A9JY-01Z-00-DX1.C10E8887-2AFA-44A5-A6E2-03456448781A
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TCGA-5P-A9KH-01Z-00-DX1.BC3B786A-28A7-46F0-9636-6937129D75F2
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TCGA-A4-7287-01Z-00-DX1.f69967b8-efb3-452d-9f0d-2ddb08cbbe08
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TCGA-A4-7732-01Z-00-DX1.e5038a3e-cdc3-4301-adcb-dfa05280389c
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TCGA-A4-8311-01Z-00-DX1.FB9D31D4-051B-48E6-BCDB-918EF8B88895
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TCGA-A4-8515-01Z-00-DX1.94C9B5F6-9F1F-4D28-8F2B-034AAE18A2CE
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TCGA-A4-A4ZT-01Z-00-DX1.AC68E09D-FE10-409E-98A5-28A54343C87E
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TCGA-A4-A57E-01Z-00-DX1.364D4762-64D9-4F71-8E33-1F6B058054F2
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TCGA-A4-A5XZ-01Z-00-DX1.91B936FA-1697-4571-800D-F45B68596A58
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TCGA-A4-A6HP-01Z-00-DX1.E323CECA-C081-4A67-98E3-B0BD28800D7A
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TCGA-AL-7173-01Z-00-DX1.91ba65ea-9eca-4ee0-b31b-95a3a27cfb81
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TCGA-AL-A5DJ-01Z-00-DX1.AF15A60D-67BF-47AB-8AE3-CCBE6DDAF86D
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TCGA-B1-A47N-01Z-00-DX1.DFEAF807-D0FB-4967-8E50-07B42ECABFE5
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TCGA-B9-4116-01Z-00-DX1.96946ff8-f67c-42ce-a75a-5e901bc02c1d
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TCGA-B9-A5W9-01Z-00-DX1.3F74793F-9362-4FBE-BDBF-1DDFB876B000
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TCGA-BQ-5877-01Z-00-DX1.3060d47f-0bc6-4a81-82ba-bf596d790f0f
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TCGA-BQ-5891-01Z-00-DX1.8e19c5b6-693e-4f65-b1ce-ab1e9ca960ec
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TCGA-BQ-7044-01Z-00-DX1.82831b80-af50-4d10-9f92-821079af66fa
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TCGA-BQ-7050-01Z-00-DX1.73bc7fb6-4eaf-464c-848a-61c0a9827251
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TCGA-BQ-7062-01Z-00-DX1.f7bc255d-3d16-4e55-a55f-b130a5e32222
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TCGA-DW-5561-01Z-00-DX1.a37c0e14-0562-414b-a467-bb171c5aed52
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TCGA-DW-7836-01Z-00-DX1.A8A28E5B-0D97-42D5-B04E-BD0D007CC30A
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TCGA-DW-7840-01Z-00-DX1.797DFE79-C506-4F49-812F-B19F3C440D7A
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TCGA-DW-7841-01Z-00-DX1.AE67F0A7-FF07-4C2E-9EF3-5CD6DEA14FE1
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TCGA-DW-7963-01Z-00-DX1.B29C89E1-FC44-4565-97EC-DCBE7C0973B1
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TCGA-G7-6792-01Z-00-DX1.450a6858-3f25-4d1e-a6f5-67289e33e3de
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TCGA-G7-6795-01Z-00-DX1.3467ceec-438d-49a2-8396-018ff67a1060
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TCGA-G7-7501-01Z-00-DX1.9d079751-475f-4e04-9c64-e626d543439d
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TCGA-G7-A8LB-01Z-00-DX1.1D3AC235-837A-4E39-9FAA-D7F4E9F5939D
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TCGA-GL-7966-01Z-00-DX1.D53E267B-BC0D-43A7-8E89-FCCC2BD5380C
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TCGA-GL-A59T-01Z-00-DX1.93128005-E121-4B81-9E9E-B05EAB93230E
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TCGA-HE-A5NH-01Z-00-DX1.EFEFC4C4-CA32-420F-A9BE-43587F15D5BD
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TCGA-HE-A5NL-01Z-00-DX1.913399D7-F66A-4163-8D98-494CFAA600EC
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TCGA-IA-A40X-01Z-00-DX1.AE832D8E-57E9-466C-B9BA-D71851219986
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TCGA-IZ-A6M8-01Z-00-DX1.72DCB378-1570-4C02-85EF-688FECA60C0E
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TCGA-MH-A562-01Z-00-DX1.422B7AF0-DF11-4B89-958D-66D0529ADB84
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TCGA-MH-A856-01Z-00-DX1.F135B458-4AB0-499D-B024-5F59E1AC6DA5
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TCGA-MH-A857-01Z-00-DX1.D51AFCBE-D104-4B54-AC38-2DBF1E0066E9
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TCGA-P4-A5E6-01Z-00-DX1.1EDE26CC-47BE-4931-807C-51D1C469670F
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TCGA-P4-AAVM-01Z-00-DX1.4F1F04A5-0D75-4EEA-93FF-0ABA77BA4D17
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TCGA-SX-A71R-01Z-00-DX1.0B4C4C57-1759-4416-85B3-EB52CD181AFA
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TCGA-SX-A71V-01Z-00-DX1.D8BA1689-B98C-4E22-AD8B-22D86F9E8D7C
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TCGA-SX-A7SM-01Z-00-DX1.567A7D93-3511-4AB5-AF26-019994048AF5
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TCGA-SX-A7SQ-01Z-00-DX1.CC608F45-80AA-49DA-9172-B706DD833889
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TCGA-SX-A7SS-01Z-00-DX1.8D9E6599-DA42-4F5B-93FF-08CBDCC77591
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TCGA-UZ-A9PL-01Z-00-DX1.A7927C7B-9304-40F4-894C-64A953D8F67B
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TCGA-UZ-A9PU-01Z-00-DX1.437BFD69-53A3-43BC-9B52-B72DA32F1888
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TCGA-Y8-A896-01Z-00-DX1.45CEAD98-A2D0-4FE0-8FD3-950119AB9BDB
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TCGA-Y8-A8S0-01Z-00-DX1.B7010362-0F7C-4094-A9AD-925443CCAC50
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slide_ids_train.txt
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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 |
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oid sha256:0f2ba7675f781319ea55715d2d333728e06905bdf20362b57aa4652815180b65
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| 3 |
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size 2655355
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