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May 9, 2024
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Update pathology tumor detection readme #584

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update pathology
yiheng-wang-nv May 9, 2024
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yiheng-wang-nv May 9, 2024
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yiheng-wang-nv May 9, 2024
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3 changes: 2 additions & 1 deletion models/pathology_tumor_detection/configs/metadata.json
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Original file line number Diff line number Diff line change
@@ -1,7 +1,8 @@
{
"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
"version": "0.5.7",
"version": "0.5.8",
"changelog": {
"0.5.8": "update readme to add memory warning",
"0.5.7": "update channel_def in metadata",
"0.5.6": "fix the wrong GPU index issue of multi-node",
"0.5.5": "modify mgpu logging level",
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6 changes: 5 additions & 1 deletion models/pathology_tumor_detection/docs/README.md
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Expand Up @@ -62,6 +62,10 @@ The training pipeline is a json file (dataset.json) which includes path to each

A probability number of the input patch being tumor or normal.

### Memory Consumption Warning

If you face memory issues in traning, you can lower the `batch_size` of the validation dataloader in the configurations to reduce the System RAM requirements.

### Inference on a WSI

Inference is performed on WSI in a sliding window manner with specified stride. A foreground mask is needed to specify the region where the inference will be performed on, given that background region which contains no tissue at all can occupy a significant portion of a WSI. Output of the inference pipeline is a probability map of size 1/stride of original WSI size.
Expand Down Expand Up @@ -126,7 +130,7 @@ python -m monai.bundle run --config_file configs/train.json --dataset_dir <actua
#### Override the `train` config to execute multi-GPU training

```
torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"
torchrun --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"
```

Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
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