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{
"cells": [
{
"cell_type": "markdown",
"id": "1ae9717c-793f-4c67-a867-df48c4210487",
"metadata": {},
"source": [
"# Talk to PathML\n",
"\n",
"### A digital pathology assistant for democratizing access to advanced computational image analysis "
]
},
{
"cell_type": "markdown",
"id": "c4f09515-97fb-4a7a-8fcb-1eba0d631d21",
"metadata": {},
"source": [
"We leveraged the recent progress in medical Large Language Models (LLMs) to create a new chat interface for those who would like to get started with PathML for advanced image analysis. This was implemented by injecting all PathML examples and documentation into a Retrieval Augmented Generation (RAG) system based on GPT-4 capabilities. Our “Digital Pathology Assistant” prototype, available [here](https://chat.openai.com/g/g-L1IbnIIVt-digital-pathology-assistant-v3-0), can be leveraged to build advanced end-to-end computational pipelines for specific use-cases. \n",
"\n",
"In this notebook, we report specific examples of how it can be used to generate specific computational pipelines for preprocessing and analyzing different types of multiplexed images. "
]
},
{
"cell_type": "markdown",
"id": "56ad469f-55ef-4fe5-919e-edc2668e6f2b",
"metadata": {},
"source": [
"## Example 1: Installing PathML on MacOS"
]
},
{
"cell_type": "markdown",
"id": "ec516a56-0cd1-4a9d-9c07-56c31c4a7921",
"metadata": {},
"source": [
" "
]
},
{
"cell_type": "markdown",
"id": "8db55b66-7c4c-4462-80f3-4b6e8a729be5",
"metadata": {},
"source": [
"## Example 2: Information about supported file types"
]
},
{
"cell_type": "markdown",
"id": "da0a5d92-06e9-40cb-ae36-db5d5d57ff8b",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "908c8357-35f7-410e-9b54-d44fae049c6b",
"metadata": {},
"source": [
"## Example 3: MIF pipelines"
]
},
{
"cell_type": "markdown",
"id": "a5dadf45-8b9b-4e5e-ac2a-cac9a61b6835",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "bc394e3c-3867-489b-9c86-6cfcef30520f",
"metadata": {},
"source": [
"## Example 4: Nucleus Detection"
]
},
{
"cell_type": "markdown",
"id": "ac8ad6d1-98d0-44e5-9048-6d7adfe66d5a",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "6242d6b7-6c9f-4857-a12d-56909225e369",
"metadata": {},
"source": [
"## Example 5: Graph API"
]
},
{
"cell_type": "markdown",
"id": "75a03e24-50fb-48a1-8db6-b8e6f831d243",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "d73742dc-c9f0-4507-a8b7-d955868ef32f",
"metadata": {},
"source": [
"## Example 6: Inference API"
]
},
{
"cell_type": "markdown",
"id": "92ac0857-fba4-45fc-b3a9-26cb97176bb7",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "198f61ef-6dd8-4d04-8168-e313e0bebcc5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "pathml_env",
"language": "python",
"name": "pathml_env"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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