review cxreportgen and minor edits to medimageinsight

Author: msakande

articles/ai-studio/how-to/healthcare-ai/deploy-cxrreportgen.md

@@ -13,7 +13,7 @@ author: msakande
 ms.custom: references_regions, generated
 ---
 
-# How to use CXRReportGen Healthcare AI Model to generate grounded findings
+# How to use CXRReportGen Healthcare AI model to generate grounded findings
 
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
@@ -26,13 +26,13 @@ In this article, you learn how to deploy CXRReportGen as an online endpoint for
 * Send test data to the model, receive, and interpret results
 
 ## CXRReportGen - grounded report generation model for chest X-rays
-Radiology reporting demands detailed image understanding, integration of multiple inputs (including comparisons with prior imaging), and precise language generation, making it an ideal candidate for generative multimodal models. CXRReportGen not only performs the task of generating a list of findings from a chest X-ray study, but also extends it by incorporating the localization of individual findings on the image—a task we refer to as grounded report generation.  
+Radiology reporting demands detailed image understanding, integration of multiple inputs (including comparisons with prior imaging), and precise language generation, making it an ideal candidate for generative multimodal models. CXRReportGen generates a list of findings from a chest X-ray study and also perform a _grounded report generation_ or _grounding_ task. That is, the CXRReportGen model also incorporates the localization of individual findings on the image. Grounding enhances the clarity of image interpretation and the transparency of AI-generated text, which end up improving the utility of automated report drafting.
 
-The following animation demonstrates the conceptual architecture of the CxrReportGen model which consists of an embedding model paired with a general reasoner large language model (LLM). 
+The following animation demonstrates the conceptual architecture of the CXRReportGen model, which consists of an embedding model paired with a general reasoner large language model (LLM). 
 
-:::image type="content" source="../../media/how-to/healthcare-ai/healthcare-reportgen.gif" alt-text="Animation of CxrReportGen architecture and data flow":::
+:::image type="content" source="../../media/how-to/healthcare-ai/healthcare-reportgen.gif" alt-text="Animation of CXRReportGen architecture and data flow.":::
 
-Grounding enhances the clarity of image interpretation and the transparency of AI-generated text, thereby improving the utility of automated report drafting. The model combines a radiology-specific image encoder with a large language model and it takes as inputs a more comprehensive set of data than many traditional approaches: the current frontal image, the current lateral image, the prior frontal image, the prior report, and the Indication, Technique, and Comparison sections of the current report. These additions significantly enhance report quality and reduce incorrect information, demonstrating the feasibility of grounded reporting as a novel and richer task in automated radiology.
+The CXRReportGen model combines a radiology-specific image encoder with a large language model and takes as inputs a more comprehensive set of data than many traditional approaches. The input data includes the current frontal image, the current lateral image, the prior frontal image, the prior report, and the indication, technique, and comparison sections of the current report. These additions significantly enhance report quality and reduce incorrect information, ultimately demonstrating the feasibility of grounded reporting as a novel and richer task in automated radiology.
 
 ## Prerequisites
 
@@ -44,18 +44,27 @@ To use CXRReportGen model with Azure AI Studio or Azure Machine Learning studio,
 
 CXRReportGen model can be deployed to our self-hosted managed inference solution, which allows you to customize and control all the details about how the model is served.  
 
-The model can be deployed through the Model Catalog UI or programmatically. In order to deploy through the UI, navigate to the [model card in the catalog](https://aka.ms/cxrreportgenmodelcard). Programmatic deployment is covered in the sample Jupyter Notebook linked at the end of this page. 
+You can deploy the model through the model catalog UI or programmatically. To deploy through the UI,
 
-For deployment to a self-hosted managed compute, you must have enough quota in your subscription. If you don't have enough quota available, you can use our temporary quota access by selecting the option **I want to use shared quota and I acknowledge that this endpoint will be deleted in 168 hours.**
+- Go to the [model card in the catalog](https://aka.ms/cxrreportgenmodelcard). 
+- On the model's overview page, select __Deploy__. 
+- If given the option to choose between serverless API deployment and deployment using a managed compute, select **Managed Compute**.
+- Fill out the details in the deployment window.
+
+    > [!NOTE]
+    > For deployment to a self-hosted managed compute, you must have enough quota in your subscription. If you don't have enough quota available, you can use our temporary quota access by selecting the option **I want to use shared quota and I acknowledge that this endpoint will be deleted in 168 hours.**
+- Select __Deploy__.
+
+To deploy the model programmatically, see [How to deploy and inference a managed compute deployment with code](../deploy-models-managed.md).
 
-> [!div class="nextstepaction"]
-> [Deploy the model to managed compute](../../concepts/deployments-overview.md)
 
 ## Work with a grounded report generation model for chest X-ray analysis
 
-### Using REST API to consume the model
+In this section, you consume the model and make basic calls to it.
+
+### Use REST API to consume the model
 
-CXRReportGen report generation model can be consumed as a REST API using simple GET requests or by creating a client like so:
+Consume the CXRReportGen report generation model as a REST API, using simple GET requests or by creating a client as follows:
 
 ```python
 from azure.ai.ml import MLClient
@@ -66,11 +75,11 @@ credential = DefaultAzureCredential()
 ml_client_workspace = MLClient.from_config(credential)
 ```
 
-In the deployment configuration you get to choose authentication method. This example uses Azure Machine Learning Token-based authentication, for more authentication options see the [corresponding documentation page](../../../machine-learning/how-to-setup-authentication.md). Also note that client is created from configuration file. This file is created automatically for Azure Machine Learning VMs. Learn more on the [corresponding API documentation page](/python/api/azure-ai-ml/azure.ai.ml.mlclient?view=azure-python#azure-ai-ml-mlclient-from-config).
+In the deployment configuration, you get to choose the authentication method. This example uses Azure Machine Learning token-based authentication. For more authentication options, see the [corresponding documentation page](../../../machine-learning/how-to-setup-authentication.md). Also note that the client is created from a configuration file that is created automatically for Azure Machine Learning virtual machines (VMs). Learn more on the [corresponding API documentation page](/python/api/azure-ai-ml/azure.ai.ml.mlclient?view=azure-python#azure-ai-ml-mlclient-from-config).
 
 ### Make basic calls to the model
 
-Once the model is deployed, you can use the following code to send data and retrieve list of findings and corresponding bounding boxes.
+Once the model is deployed, use the following code to send data and retrieve a list of findings and corresponding bounding boxes.
 
 ```python
 input_data = {
@@ -120,11 +129,11 @@ The `input_data` object contains the following fields:
 | Key           | Type           | Required/Default | Allowed values    | Description |
 | ------------- | -------------- | :-----------------:| ----------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `columns`       | `list[string]`       | Y    |  `"frontal_image"`, `"lateral_image"`, `"prior_image"`,`"indication"`, `"technique"`,  `"comparison"`, `"prior_report"`  | An object containing the strings mapping data to inputs passed to the model.|
-| `index`   | `integer` | Y | 0 - 10 | Count of inputs passed to the model. You are limited by how much GPU RAM you have on the VM where CxrReportGen is hosted and by how much data can be passed in a single POST request which will depend on the size of your images, so it's reasonable to keep this number under 10. Check model logs if you're getting errors when passing multiple inputs. |
-| `data`   | `list[list[string]]` | Y | "" | The list contains the list of items passed to the model. Length of the list is defined by the index parameter. Each item is a list of several strings, order and meaning is defined by the "columns" parameter. The text strings contain text, the image strings are the image bytes encoded using base64 and decoded as utf-8 string |
+| `index`   | `integer` | Y | 0 - 10 | Count of inputs passed to the model. You're limited by how much GPU RAM you have on the VM where CxrReportGen is hosted, and by how much data can be passed in a single POST request—which depends on the size of your images. Therefore, it's reasonable to keep this number under 10. Check model logs if you're getting errors when passing multiple inputs. |
+| `data`   | `list[list[string]]` | Y | "" | The list contains the list of items passed to the model. The length of the list is defined by the index parameter. Each item is a list of several strings. The order and meaning are defined by the `columns` parameter. The text strings contain text. The image strings are the image bytes encoded using base64 and decoded as utf-8 string |
 
 
-### Request Example
+### Request example
 
 **A simple inference requesting list of findings for a single frontal image with no indication provided** 
 ```JSON
@@ -187,10 +196,9 @@ Response payload is a JSON formatted string containing the following fields:
 The deployed model API supports images encoded in PNG or JPEG formats. For optimal results, we recommend using uncompressed/lossless PNGs with 8-bit monochromatic images.
 
 ## Learn more from samples
-CXRReportGen is a versatile model that can be applied to a wide range of tasks and imaging modalities. For more examples see the following interactive Python Notebooks: 
+CXRReportGen is a versatile model that can be applied to a wide range of tasks and imaging modalities. For more examples see the following interactive Python notebook: 
 
-### Getting started
-* [Deploying and Using CXRReportGen](https://aka.ms/healthcare-ai-examples-cxr-deploy): learn how to deploy the CXRReportGen model and integrate it into your workflow. This notebook also covers bounding box parsing and visualization techniques.
+* [Deploying and Using CXRReportGen](https://aka.ms/healthcare-ai-examples-cxr-deploy): Learn how to deploy the CXRReportGen model and integrate it into your workflow. This notebook also covers bounding-box parsing and visualization techniques.
 
 ## Related content
 

articles/ai-studio/how-to/healthcare-ai/deploy-medimageinsight.md

@@ -29,7 +29,7 @@ MedImageInsight foundational model for health is a powerful model that can proce
 
 An embedding model is capable of serving as the basis of many different solutions—from classification to more complex scenarios like group matching or outlier detection. The following animation shows an embedding model being used for image similarity search and to detect images that are outliers.
 
-:::image type="content" source="../../media/how-to/healthcare-ai/healthcare-embedding-capabilities.gif" alt-text="Animation that shows an embedding model capable of supporting similarity search and quality control scenarios":::
+:::image type="content" source="../../media/how-to/healthcare-ai/healthcare-embedding-capabilities.gif" alt-text="Animation that shows an embedding model capable of supporting similarity search and quality control scenarios.":::
 
 ## Prerequisites
 
@@ -72,7 +72,7 @@ credential = DefaultAzureCredential()
 ml_client_workspace = MLClient.from_config(credential)
 ```
 
-In the deployment configuration, you get to choose the authentication method. This example uses Azure Machine Learning Token-based authentication. For more authentication options, see the [corresponding documentation page](../../../machine-learning/how-to-setup-authentication.md). Also note the client is created from a configuration file that is created automatically for Azure Machine Learning virtual machines (VMs). Learn more on the [corresponding API documentation page](/python/api/azure-ai-ml/azure.ai.ml.mlclient?view=azure-python#azure-ai-ml-mlclient-from-config).
+In the deployment configuration, you get to choose the authentication method. This example uses Azure Machine Learning token-based authentication. For more authentication options, see the [corresponding documentation page](../../../machine-learning/how-to-setup-authentication.md). Also note that the client is created from a configuration file that is created automatically for Azure Machine Learning virtual machines (VMs). Learn more on the [corresponding API documentation page](/python/api/azure-ai-ml/azure.ai.ml.mlclient?view=azure-python#azure-ai-ml-mlclient-from-config).
 
 ### Make basic calls to the model
 
@@ -137,7 +137,7 @@ The `input_data` object contains the following fields:
 | ------------- | -------------- | :-----------------:| ----------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `columns`       | `list[string]`       | Y    |  `"text"`, `"image"` | An object containing the strings mapping data to inputs passed to the model.|
 | `index`   | `integer` | Y | 0 - 1024| Count of inputs passed to the model. You're limited by how much data can be passed in a single POST request, which depends on the size of your images. Therefore, you should keep this number in the dozens |
-| `data`   | `list[list[string]]` | Y | "" | The list contains the items passed to the model which is defined by the index parameter. Each item is a list of two strings. The order is defined by the "columns" parameter. The `text` string contains text to embed. The `image` strings are the image bytes encoded using base64 and decoded as utf-8 string |
+| `data`   | `list[list[string]]` | Y | "" | The list contains the items passed to the model which is defined by the index parameter. Each item is a list of two strings. The order is defined by the `columns` parameter. The `text` string contains text to embed. The `image` strings are the image bytes encoded using base64 and decoded as utf-8 string |
 
 The `params` object contains the following fields:
 
@@ -220,15 +220,15 @@ The preferred compression format is lossless PNG, containing either an 8-bit mon
 ## Learn more from samples
 MedImageInsight is a versatile model that can be applied to a wide range of tasks and imaging modalities. For more specific examples of solving various tasks with MedImageInsight, see the following interactive Python notebooks. 
 
-### Getting started
+#### Getting started
 * [Deploying and Using MedImageInsight](https://aka.ms/healthcare-ai-examples-mi2-deploy): Learn how to deploy the MedImageInsight model programmatically and issue an API call to it.
 
-### Classification techniques
+#### Classification techniques
 * [Building a Zero-Shot Classifier](https://aka.ms/healthcare-ai-examples-mi2-zero-shot): Discover how to use MedImageInsight to create a classifier without the need for training or large amount of labeled ground truth data.
 
 * [Enhancing Classification with Adapter Networks](https://aka.ms/healthcare-ai-examples-mi2-adapter): Improve classification performance by building a small adapter network on top of MedImageInsight.
 
-### Advanced applications
+#### Advanced applications
 * [Inferring MRI Acquisition Parameters from Pixel Data](https://aka.ms/healthcare-ai-examples-mi2-exam-parameter): Understand how to extract MRI exam acquisition parameters directly from imaging data.
 
 * [Scalable MedImageInsight Endpoint Usage](https://aka.ms/healthcare-ai-examples-mi2-advanced-call): Learn how to generate embeddings of medical images at scale using the MedImageInsight API while handling potential network issues gracefully.