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+ "448d080044d44b71ba84f3918e7a1772": {
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+ "_view_module_version": "1.2.0",
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+ "description_width": ""
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+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install datasets"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "v9Sz-h5lsv0a",
+ "outputId": "c874dae3-738c-49a9-91a9-d81ffcc6038f"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting datasets\n",
+ " Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.1)\n",
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
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+ "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "Collecting multiprocess (from datasets)\n",
+ " Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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+ "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.3)\n",
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+ "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
+ "Installing collected packages: dill, multiprocess, datasets\n",
+ "Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install SimpleITK"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "PuwaOuV0HD63",
+ "outputId": "666f4180-6b46-4c4b-d65d-cc3229ab9175"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting SimpleITK\n",
+ " Downloading SimpleITK-2.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (52.7 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.7/52.7 MB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: SimpleITK\n",
+ "Successfully installed SimpleITK-2.3.1\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# @title Test if your dataset works!\n",
+ "from datasets import load_dataset\n",
+ "\n",
+ "dataset = load_dataset(\"Euniceyeee/kidney-ct-abnormality\")"
+ ],
+ "metadata": {
+ "id": "tjIPlD75YkIX",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 408,
+ "referenced_widgets": [
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+ ]
+ },
+ "outputId": "176d8e4c-fb66-42cf-be6a-a4336b2524e1"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n",
+ "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1461: FutureWarning: The repository for Euniceyeee/kidney-ct-abnormality contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/Euniceyeee/kidney-ct-abnormality\n",
+ "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+ "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading builder script: 0%| | 0.00/4.09k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "563af9c8e4ce4817b1404f9a6c7152b9"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading readme: 0%| | 0.00/7.58k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "5f3926f5f3c446f4bcaed22eac1916e5"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data: 0%| | 0.00/78.7k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ "version_minor": 0,
+ "model_id": "bf07e0beff4d4c95ba181eaecc356490"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data: 0%| | 0.00/3.51G [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "6072d07893d6485a9f426e6f6d9363d7"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4d1b49a8958148e0ab98c3a60bf98809"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating test split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7ac818073eca44a4990fc5d70c065585"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset"
+ ],
+ "metadata": {
+ "id": "_iDTyZNinahc",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "974df833-7473-4a1e-a941-b981daa0f65d"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['images', 'img_path', 'label'],\n",
+ " num_rows: 788\n",
+ " })\n",
+ " test: Dataset({\n",
+ " features: ['images', 'img_path', 'label'],\n",
+ " num_rows: 198\n",
+ " })\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "###Example:
\n",
+ "images: a sequence of CT images as the original CT images have multilayers(already converted to PIL compatible format).
\n",
+ "img_path: the original .mha image file path (if visualized, it has higher resolution) (the numpy image array dtype is float64)
\n",
+ "label: either 0 or 1, indicating abnormality (0: normal, 1: abnormal)"
+ ],
+ "metadata": {
+ "id": "8bquVX2Fz3DQ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "next(iter((dataset['train'])))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "nHj-kBJCXjbm",
+ "outputId": "e7a510cf-fa8e-4a31-c1aa-4446e0bc7f27"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'images': [,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
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+ " ,\n",
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+ " ,\n",
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+ " ,\n",
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+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ],\n",
+ " 'img_path': '/root/.cache/huggingface/datasets/downloads/extracted/d0b4cca623b19de5f8d73747a149122c28ef63d9f1cb97a354f158b3351cccdd/kidney_CT/train/kidneyabnormalityKiTS-0000_0000.mha',\n",
+ " 'label': 0}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 5
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import math"
+ ],
+ "metadata": {
+ "id": "Coyw-9K6knE2"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Images:"
+ ],
+ "metadata": {
+ "id": "DUFkk2Vt4HRh"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "images = next(iter((dataset['train'])))['images']\n",
+ "num_images = len(images)\n",
+ "grid_size = math.ceil(math.sqrt(num_images))\n",
+ "fig, axs = plt.subplots(nrows=grid_size, ncols=grid_size, figsize=(20, 20))\n",
+ "axs = axs.flatten()\n",
+ "for i, img in enumerate(images):\n",
+ " axs[i].imshow(img, cmap='gray')\n",
+ " # axs[i].axis('off')\n",
+ "for j in range(i + 1, grid_size**2):\n",
+ " axs[j].axis('off')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "Krrf6xmykp1p",
+ "outputId": "4366b2ed-889d-446a-f7d0-f6ea3e3dcd3d"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "