From 61f295d201421c84c0650966486f74d83f57c378 Mon Sep 17 00:00:00 2001
From: Soumik Rakshit <19soumik.rakshit96@gmail.com>
Date: Tue, 16 Jan 2024 12:00:25 +0000
Subject: [PATCH] update: add setup and intro

---
 .../monai/3d_brain_tumor_segmentation.ipynb   | 58 ++++++++++++++++++-
 1 file changed, 56 insertions(+), 2 deletions(-)

diff --git a/colabs/monai/3d_brain_tumor_segmentation.ipynb b/colabs/monai/3d_brain_tumor_segmentation.ipynb
index c553b3e2..b0a6149f 100644
--- a/colabs/monai/3d_brain_tumor_segmentation.ipynb
+++ b/colabs/monai/3d_brain_tumor_segmentation.ipynb
@@ -1,12 +1,47 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Brain tumor 3D segmentation with MONAI and Weights & Biases\n",
+    "\n",
+    "This tutorial shows how to construct a training workflow of multi-labels segmentation task using [MONAI](https://github.com/Project-MONAI/MONAI) and use experiment tracking and data visualization features of [Weights & Biases](https://wandb.ai/site). The tutorial contains the following features:\n",
+    "\n",
+    "1. Initialize a Weights & Biases run and synchrozize all configs associated with the run for reproducibility.\n",
+    "2. MONAI transform API:\n",
+    "    1. MONAI Transforms for dictionary format data.\n",
+    "    2. How to define a new transform according to MONAI transform API.\n",
+    "    3. How to randomly adjust intensity for data augmentation.\n",
+    "3. Data Loading and Visualization:\n",
+    "    1. Load Nifti image with metadata, load a list of images and stack them.\n",
+    "    2. Cache IO and transforms to accelerate training and validation.\n",
+    "    3. Visualize the data using `wandb.Table` and interactive segmentation overlay on Weights & Biases.\n",
+    "4. Training a 3D `SegResNet` model\n",
+    "    1. Using the `networks`, `losses`, and `metrics` APIs from MONAI.\n",
+    "    2. Training the 3D `SegResNet` model using a PyTorch training loop.\n",
+    "    3. Track the training experiment using Weights & Biases.\n",
+    "    4. Log and version model checkpoints as model artifacts on Weights & Biases.\n",
+    "5. Visualize and compare the predictions on the validation dataset using `wandb.Table` and interactive segmentation overlay on Weights & Biases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup and Installation\n",
+    "\n",
+    "First, let us install the latest version of both MONAI and Weights and Biases."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q \"monai-weekly[nibabel, tqdm]\" wandb"
+    "!python -c \"import monai\" || pip install -q -U \"monai[nibabel, tqdm]\"\n",
+    "!python -c \"import wandb\" || pip install -q -U wandb"
    ]
   },
   {
@@ -24,6 +59,7 @@
     "from monai.apps import DecathlonDataset\n",
     "from monai.data import DataLoader, decollate_batch\n",
     "from monai.losses import DiceLoss\n",
+    "from monai.config import print_config\n",
     "from monai.inferers import sliding_window_inference\n",
     "from monai.metrics import DiceMetric\n",
     "from monai.networks.nets import SegResNet\n",
@@ -45,7 +81,25 @@
     ")\n",
     "from monai.utils import set_determinism\n",
     "\n",
-    "import torch"
+    "import torch\n",
+    "\n",
+    "print_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will then authenticate this colab instance to use W&B."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wandb.login()"
    ]
   },
   {