Add Open in Colab badge to multimodal early-fusion tutorial

Links to the GitHub-hosted notebook path (colab.research.google.com/github/...)
so it stays in sync with the repo and resolves once merged to main,
matching the convention used by other tutorials in this repo.

Signed-off-by: Paul Sentongo <134306188+sentongo-web@users.noreply.github.com>

Author: sentongo-web

multimodal/nakaseke_multimodal_early_fusion/README.md

@@ -1,5 +1,7 @@
 # Multimodal Early-Fusion Network: Radiographs + Clinical Tabular Data
 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/multimodal/nakaseke_multimodal_early_fusion/multimodal_early_fusion_tutorial.ipynb)
+
 This tutorial demonstrates an **early-fusion** architecture that combines a 2D medical image stream
 with a low-dimensional clinical tabular stream in a single MONAI dictionary-based pipeline, using
 [`multimodal_early_fusion_tutorial.ipynb`](./multimodal_early_fusion_tutorial.ipynb).

multimodal/nakaseke_multimodal_early_fusion/multimodal_early_fusion_tutorial.ipynb

@@ -17,6 +17,8 @@
     "\n",
     "# Multimodal Early-Fusion Network: Radiographs + Clinical Tabular Data\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/multimodal/nakaseke_multimodal_early_fusion/multimodal_early_fusion_tutorial.ipynb)\n",
+    "\n",
     "This tutorial builds an end-to-end **early-fusion** classifier that combines a 2D medical image stream with a low-dimensional clinical tabular stream, using only MONAI's dictionary-based transform and dataset APIs plus a small PyTorch fusion network.\n",
     "\n",
     "The clinical schema (age, BMI, salivary pH, systolic blood pressure) and the binary screening task are modeled after a real hypertension-screening workflow at Nakaseke Hospital, Uganda. Because that patient data is confidential and cannot be published, **this notebook generates a fully synthetic cohort locally** -- no downloads, no external services, no real patient data -- while keeping the same feature schema, tensor shapes, and modeling problem, so the pipeline is a drop-in template for a real (IRB-approved, de-identified) dataset with matching keys.\n",