Add multimodal early-fusion tutorial (radiograph + clinical tabular)

Adds a self-contained tutorial fusing a 2D image stream (MONAI
DenseNet121) with a low-dimensional clinical tabular stream via
dictionary-based transforms and torch.cat, using a fully synthetic,
locally generated dataset modeled after a Nakaseke Hospital
hypertension-screening schema. No downloads or real patient data.

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

Author: sentongo-web

README.md

@@ -235,6 +235,10 @@ This tutorial demonstrates how to construct a training workflow of [HoVerNet](ht
 ##### [Nuclei Classification](./pathology/nuclick#nuclei-classification-model)
 The notebook demonstrates examples of training and inference pipelines with interactive annotation for pathology, NuClick is used for delineating nuclei, cells and a squiggle for outlining glands.
 
+#### <ins>**Multimodal**</ins>
+##### [Multimodal Early-Fusion Network](./multimodal/nakaseke_multimodal_early_fusion)
+An end-to-end tutorial fusing a 2D radiograph stream (MONAI `DenseNet121`) with a low-dimensional clinical tabular stream via dictionary-based transforms and `torch.cat`, using a fully synthetic, locally generated dataset.
+
 #### <ins>**Acceleration**</ins>
 ##### [fast_model_training_guide](./acceleration/fast_model_training_guide.md)
 The document introduces details of how to profile the training pipeline, how to analyze the dataset and select suitable algorithms, and how to optimize GPU utilization in single GPU, multi-GPUs or even multi-nodes.

multimodal/nakaseke_multimodal_early_fusion/README.md

@@ -0,0 +1,52 @@
+# Multimodal Early-Fusion Network: Radiographs + Clinical Tabular Data
+
+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).
+
+## Motivation
+
+Most MONAI tutorials focus on a single imaging modality. In real deployments, especially in
+resource-constrained clinics, an image is rarely read in isolation -- a clinician also has vitals,
+labs, or a short structured history on hand, and a model that ignores that context is throwing away
+signal it doesn't have to. This tutorial shows the minimum end-to-end MONAI/PyTorch pattern for
+combining the two without hand-rolling a custom `Dataset`.
+
+The clinical feature schema (age, BMI, salivary pH, systolic blood pressure) and the binary screening
+task are modeled after a hypertension-screening workflow explored at Nakaseke Hospital, Uganda. The
+underlying patient data is confidential and is **not** included in or downloaded by this tutorial.
+
+## Dataset
+
+**Fully synthetic, generated locally, no download required.** The notebook's
+`simulate_nakaseke_multimodal_dataset()` function creates, on the fly:
+
+- one synthetic 2D radiograph per patient, saved as a NIfTI (`.nii.gz`) file with `nibabel`
+- a 4-dimensional tabular vector `[age, bmi, salivary_ph, systolic_bp]`
+- a binary label
+
+Both modalities are generated from a shared hidden "risk factor" per patient, so neither the image
+nor the tabular vector is fully predictive of the label on its own -- this is what motivates fusing
+them. There is no claim that the notebook's results reflect real-world diagnostic performance; the
+synthetic cohort exists only to exercise the pipeline end-to-end without any real or downloadable
+data.
+
+## What the notebook covers
+
+1. Reproducible setup with `monai.utils.set_determinism`.
+2. Synthetic multimodal data generation and a MONAI-style data manifest
+   (`{"image": ..., "nakaseke_tabular": ..., "label": ...}`).
+3. A dictionary-based `Compose` pipeline where image-only transforms (`LoadImaged`,
+   `EnsureChannelFirstd`, `ScaleIntensityRanged`) touch only the `"image"` key, while the tabular and
+   label keys are cast to tensors with `EnsureTyped` and otherwise left untouched.
+4. `ResilientMultimodalClassifier`: a MONAI `DenseNet121` visual stream (512-d embedding) fused via
+   `torch.cat` with a small tabular projection stream (16-d embedding) into a 528-d representation,
+   followed by a dropout-regularized classification head.
+5. A short training loop (`max_epochs`, `val_interval`) showing validation accuracy improve as the
+   model learns to use both streams.
+
+## Requirements
+
+Everything needed is installed by the notebook's own `Setup environment` cell
+(`monai-weekly[nibabel, tqdm]`, `matplotlib`). No GPU is required -- the default image size (64x64)
+and cohort size (200 patients) are chosen to train in well under a minute on CPU.