Fix more broken links and anchors in README files

CONTRIBUTING.md

@@ -186,7 +186,7 @@ By making a contribution to this project, I certify that:
 Once the changes are ready, push your local branch to the remote, e.g. `git push --set-upstream origin <your new branch name>`, and [open a pull request](https://github.com/Project-MONAI/tutorials/pulls).
 When the pull request is opened, the MONAI repository has a set of GitHub actions that will run checks on the changes.
 
-Please check more [details in the guidelines](#ci-test-passing-guide) for how to pass the tests.
+Please check more [details in the guidelines](#cicd-test-passing-guide) for how to pass the tests.
 
 In addition, the team will perform diligent code reviews following this [set of guidelines](#common-recommendations-for-the-review) to reduce the amount of work for users to run the tutorials.
 

bundle/introducing_config/README.md

@@ -155,5 +155,5 @@ plt.show()
 - Running customized Python components (made available on the `PYTHONPATH`).
 - Overriding the component in `example.yaml` using, for example, `--id=new_value` in the command line.
 - Multiple configuration files and cross-file references.
-- Replacing in terms of plain texts instead of Python objects ([tutorial](../get_started.md)).
+- Replacing in terms of plain texts instead of Python objects ([tutorial](../further_features.md)).
 - The debugging mode to investigate the intermediate variables and results.

deepgrow/ignite/README.md

@@ -143,7 +143,7 @@ This notebook helps to run any pre-transforms before running inference over a De
 It also helps to run post-transforms to get the final label mask.
 
 
-#### [Deepgrow Stats](./handler.py)
+#### Deepgrow Stats
 It contains basic ignite handler to capture region/organ-wise statistics, save snapshots, outputs while running train/validation over a dataset that has multi-label mask.
 By-default the handler is added as part of training/validation steps.
 

deployment/Triton/README.md

@@ -53,7 +53,7 @@ This demo and description borrows heavily from the [Triton Python Backend](https
 
 Get The Demo Source Code
 -------------------------------
-Pull down the demo repository and start with the [Quick Start] (#quick-start) guide.
+Pull down the demo repository and start with the [Quick Start](#quick-start) guide.
 
 ```
 $ git clone https://github.com/Project-MONAI/tutorials.git
@@ -69,7 +69,7 @@ any C++ code. We will use this to demonstrate implementing MONAI code inside Tri
 * [Quick Start](#quick-start)
 * [Examples](#examples)
 * [Usage](#usage)
-* [Model Config File](#model-config-file)
+* [Model Config File](#model-config)
 * [Error Hanldling](#error-handling)
 
 
@@ -106,7 +106,6 @@ Alternatively, the user can just run the shell script provided the previous step
 $ ./mednist_client_run.sh
 ```
 The expected result is variety of classification results for body images and local inference times.
-```
 
 ## Examples:
 The example demonstrates running a Triton Python Backend on a single image classification problem.
@@ -130,14 +129,14 @@ COPY models/monai_covid/1/model.py /models/monai_covid/1
 
 ENTRYPOINT [ "tritonserver", "--model-repository=/models"]
 ```
-Note: The Triton service expects a certain directory structure discussed in [Model Config File](#model-config-file) to load the model definitions.
+Note: The Triton service expects a certain directory structure discussed in [Model Config File](#model-config) to load the model definitions.
 
 2. Next, the container with the Triton Service runs as a service (in background or separate terminal for demo).  In this example, the ports used by the Triton Service are set to `8000` for client communications.
 ```Dockerfile:
 demo_app_image_name="monai_triton:demo"
 docker run --shm-size=128G --rm -p 127.0.0.1:8000:8000 -p 127.0.0.1:8001:8001 -p 127.0.0.1:8090:8002 ${demo_app_image_name}
 ```
-3. See [Model Config File](#model-config-file) to see the expected file structure for Triton.
+3. See [Model Config File](#model-config) to see the expected file structure for Triton.
 - Modify the models/monai_prostrate/1/model.py file to satisfy any model configuration requirements while keeping the required components in the model definition. See the * [Usage](#usage) section for background.
 - In the models/monai_prostrate/1/config.pbtxt file configure the number of GPUs and which ones are used.
 e.g. Using two available GPUs and two parallel versions of the model per GPU

full_gpu_inference_pipeline/README.md

@@ -11,7 +11,7 @@
   * [Benchmark](#benchmark)
     + [Understanding the benchmark output](#understanding-the-benchmark-output)
     + [HTTP vs. gRPC vs. shared memory](#http-vs-grpc-vs-shared-memory)
-    + [Pre/Post-processing on GPU vs. CPU](#pre-post-processing-on-gpu-vs-cpu)
+    + [Pre/Post-processing on GPU vs. CPU](#prepost-processing-on-gpu-vs-cpu)
 
 ## Overview
 
@@ -57,7 +57,7 @@ Triton environment can be quickly setup by running a Triton docker container:
 docker run --gpus=1 -it --name='triton_monai' --ipc=host -p18100:8000 -p18101:8001 -p18102:8002 --shm-size=1g -v /yourfolderpath:/triton_monai nvcr.io/nvidia/tritonserver:21.12-py3
 ```
 Please note that when starting the docker container, --ipc=host should be set, so that shared memory can be used to do the data transmission between server and client. Also you should allocate a relatively large shared memory using --shm-size option, because starting from 21.04 release, Python backend uses shared memory to connect user's code to Triton.
-### Setup python execution environment
+### Setup python execution environment
 Since we will use MONAI transforms in Triton python backend, we should set up the python execution environment in Triton container by following the instructions in [Triton python backend repository](https://github.com/triton-inference-server/python_backend#using-custom-python-execution-environments). For the installation steps of MONAI, you can refer to [monai install](https://docs.monai.io/en/latest/installation.html "monai install"). Below are the steps used to setup the proper environments for this experiment:
 
 Install the software packages below:
@@ -140,7 +140,7 @@ From the result, we can come to a conclusion that using shared memory will great
 
 ![](../figures/full_gpu_inference_figure1.png)
 
-### Pre/Post-processing on GPU vs. CPU 
+### Pre/Post-processing on GPU vs. CPU
 After doing pre and post-processing on GPU, we can get a 12x speedup for the full pipeline.
 
 ![](../figures/full_gpu_inference_figure2.png)

nnunet/README.md

@@ -52,7 +52,7 @@ The installation instruction is described [here](docs/install.md).
 
 The user needs to provide a data list (".json" file) for the new task and data root. In general, a valid data list needs to follow the format of the ones in [Medical Segmentation Decathlon (MSD)](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2).
 
-In [this tutorial](../auto3dseg/notebooks/msd_datalist_generator.ipynb), we provided example steps to download the [MSD Spleen dataset](http://medicaldecathlon.com) and prepare a datalist.
+In [this tutorial](../auto3dseg/notebooks/msd_crossval_datalist_generator.ipynb), we provided example steps to download the [MSD Spleen dataset](http://medicaldecathlon.com) and prepare a datalist.
 Below we assume the dataset is downloaded to `/workspace/data/Task09_Spleen` and the datalist is in the current directory.
 
 ### Run with Minimal Input using ```nnUNetV2Runner```

reconstruction/MRI_reconstruction/unet_demo/README.md

@@ -19,7 +19,7 @@ This folder contains code to train and validate a U-Net for accelerated MRI reco
 
 * [Inference](#Inference)
 
-* [Acknowledgment](#Acknowledgement)
+* [Acknowledgment](#acknowledgment)
 
 ***
 

reconstruction/MRI_reconstruction/varnet_demo/README.md

@@ -19,7 +19,7 @@ This folder contains code to train and validate an e2e-VarNet ([https://arxiv.or
 
 * [Inference](#Inference)
 
-* [Acknowledgment](#Acknowledgement)
+* [Acknowledgment](#acknowledgment)
 
 ***