Upload example.ipynb

example.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🧠 NOVA Benchmark: Extreme Stress-Test for Out-of-Distribution Detection in Brain MRI\n",
+    "\n",
+    "Welcome to the NOVA dataset — a carefully curated, evaluation-only benchmark designed to push the limits of machine learning models in real-world clinical scenarios. With over **900 brain MRI scans**, **281 rare pathologies**, and **rich clinical metadata**, NOVA goes beyond traditional anomaly detection.\n",
+    "\n",
+    "This notebook walks you through how to:\n",
+    "\n",
+    "- Load the NOVA dataset directly from Hugging Face 🤗\n",
+    "- Access images, captions, and diagnostic metadata\n",
+    "- Visualize expert-annotated bounding boxes (gold standard and raters)\n",
+    "- Explore one of the most challenging testbeds for generalization and reasoning under uncertainty\n",
+    "\n",
+    "> ⚠️ This benchmark is intended **only for evaluation**. No training should be performed on NOVA.\n",
+    "\n",
+    "📘 For more details, visit the [dataset page on Hugging Face](https://huggingface.co/datasets/Ano-2090/Nova).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "from datasets import load_dataset\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load dataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = load_dataset(\"Ano-2090/Nova\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Select a random example\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example = random.choice(ds[\"test\"])\n",
+    "image = example[\"image\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create figure and display image\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, figsize=(8, 8))\n",
+    "ax.imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot gold standard bounding boxes (gold)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bbox = example[\"bbox_gold\"]\n",
+    "for x, y, w, h in zip(bbox[\"x\"], bbox[\"y\"], bbox[\"width\"], bbox[\"height\"]):\n",
+    "    rect = patches.Rectangle((x, y), w, h, linewidth=2, edgecolor=\"gold\", facecolor=\"none\")\n",
+    "    ax.add_patch(rect)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot rater bounding boxes (turquoise, salmon) with labels\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = ['#40E0D0', '#FA8072']\n",
+    "raters = example[\"bbox_raters\"]\n",
+    "if raters:\n",
+    "    for i in range(len(raters[\"x\"])):\n",
+    "        rater = raters[\"rater\"][i]\n",
+    "        x = raters[\"x\"][i]\n",
+    "        y = raters[\"y\"][i]\n",
+    "        w = raters[\"width\"][i]\n",
+    "        h = raters[\"height\"][i]\n",
+    "        rect = patches.Rectangle((x, y), w, h, linewidth=1.5, edgecolor=colors[i], facecolor=\"none\", linestyle=\"--\")\n",
+    "        ax.add_patch(rect)\n",
+    "        if i == 0:\n",
+    "            ax.text(x, y - 5, rater, color=colors[i], fontsize=8, weight=\"bold\", va=\"bottom\")\n",
+    "        else: \n",
+    "            ax.text(x + w/2, y + h + 15, rater, color=colors[i], fontsize=8, weight=\"bold\", va=\"bottom\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize example\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.title(f'{example[\"filename\"]} — {example[\"final_diagnosis\"]}', fontsize=12)\n",
+    "plt.axis(\"off\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print other metadata \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('*-------------------------------------------------------*')\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('caption:', example[\"caption\"])\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('clinical history:', example[\"clinical_history\"])\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('differential diagnosis:', example[\"differential_diagnosis\"])\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('final diagnosis:', example[\"final_diagnosis\"])\n",
+    "print('*-------------------------------------------------------*')\n",
+    "print('*-------------------------------------------------------*')"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}