Update pathology tumor detection readme (#584)

Fixes # .

### Description
This bundle takes large system RAM, thus It would be helpful to add a
warning.

### Status
**Ready**

### Please ensure all the checkboxes:
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Codeformat tests passed locally by running `./runtests.sh
--codeformat`.
- [ ] In-line docstrings updated.
- [ ] Update `version` and `changelog` in `metadata.json` if changing an
existing bundle.
- [ ] Please ensure the naming rules in config files meet our
requirements (please refer to: `CONTRIBUTING.md`).
- [ ] Ensure versions of packages such as `monai`, `pytorch` and `numpy`
are correct in `metadata.json`.
- [ ] Descriptions should be consistent with the content, such as
`eval_metrics` of the provided weights and TorchScript modules.
- [ ] Files larger than 25MB are excluded and replaced by providing
download links in `large_file.yml`.
- [ ] Avoid using path that contains personal information within config
files (such as use `/home/your_name/` for `"bundle_root"`).

---------

Signed-off-by: Yiheng Wang <[email protected]>

Author: yiheng-wang-nv

models/pathology_tumor_detection/configs/metadata.json

@@ -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",

models/pathology_tumor_detection/docs/README.md

@@ -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` 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.