Preprocessing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ChloeZhou1997/BreastCancerCNN/blob/main/Preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "q40Ju-bhfsQV",
      "metadata": {
        "id": "q40Ju-bhfsQV"
      },
      "source": [
        "# Demo of single images"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5ChCI2yVfzA4",
      "metadata": {
        "id": "5ChCI2yVfzA4"
      },
      "source": [
        "## Prep\n",
        "\n",
        "1. connect to google drive where data are stored\n",
        "2. install *pylibjpeg* and *pydicom* according to the [duke repo](https://github.com/MaciejMazurowski/duke-dbt-data)\n",
        "3. get helper function from duck repo mentioned before (the image is 3D and cannot be displayed using conventional python liberary) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "XuqN0vlFlo5D",
      "metadata": {
        "id": "XuqN0vlFlo5D"
      },
      "outputs": [],
      "source": [
        "# import helper\n",
        "from helper import dcmread_image, read_boxes, draw_box\n",
        "import os\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from numpy import random\n",
        "from pathlib import Path"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "J7U-ri_Mg2QH",
      "metadata": {
        "id": "J7U-ri_Mg2QH"
      },
      "source": [
        "## Show a single image of the breast"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Xt62zngjK6py",
      "metadata": {
        "id": "Xt62zngjK6py"
      },
      "outputs": [],
      "source": [
        "# This function is used to modify the path of the data\n",
        "\n",
        "def change_path(path):\n",
        "  count = 0\n",
        "  for index in reversed(range(len(path))):\n",
        "    if path[index] == '-':\n",
        "      count += 1\n",
        "      if count == 2: \n",
        "        path = path[:index] + '-NA' + path[index:]\n",
        "        break\n",
        "\n",
        "  return path\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "B2skI2Su4uQ1",
      "metadata": {
        "id": "B2skI2Su4uQ1"
      },
      "outputs": [],
      "source": [
        "# generate random picture to view\n",
        "\n",
        "def view_image_rand(path):\n",
        "\n",
        "#   file_path = path\n",
        "  file_path = Path(path)\n",
        "  df = pd.read_csv(\"BCS-DBT file-paths-train-v2.csv\")\n",
        "  rand_index = random.randint(len(df))\n",
        "  view_series = df.iloc[rand_index]\n",
        "  spec_path = view_series['descriptive_path']\n",
        "  spec_path = change_path(spec_path)\n",
        "  view = view_series[\"View\"]\n",
        "  path = file_path / spec_path\n",
        "#   path = os.path.join(file_path,spec_path)\n",
        "  image = dcmread_image(fp=path, view=view)\n",
        "  print('Patient ID: ' + view_series['PatientID'] + '  View:' + view + '  Index Number: ' , rand_index)\n",
        "  plt.imshow(image[0], cmap=plt.cm.gray)\n",
        "\n",
        "  return path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f057a5f2",
      "metadata": {
        "id": "f057a5f2",
        "outputId": "888c4946-5bfa-444a-c039-db85bba558ff"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PatientID</th>\n",
              "      <th>StudyUID</th>\n",
              "      <th>View</th>\n",
              "      <th>descriptive_path</th>\n",
              "      <th>classic_path</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DBT-P00013</td>\n",
              "      <td>DBT-S00163</td>\n",
              "      <td>rmlo</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00013/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00013/1.2.826...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DBT-P00023</td>\n",
              "      <td>DBT-S04378</td>\n",
              "      <td>lcc</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00023/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00023/1.2.826...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DBT-P00023</td>\n",
              "      <td>DBT-S04378</td>\n",
              "      <td>lmlo</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00023/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00023/1.2.826...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DBT-P00023</td>\n",
              "      <td>DBT-S04378</td>\n",
              "      <td>rcc</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00023/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00023/1.2.826...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DBT-P00023</td>\n",
              "      <td>DBT-S04378</td>\n",
              "      <td>rmlo</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00023/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P00023/1.2.826...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19143</th>\n",
              "      <td>DBT-P04356</td>\n",
              "      <td>DBT-S00073</td>\n",
              "      <td>rmlo</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P04356/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P04356/1.2.826...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19144</th>\n",
              "      <td>DBT-P01481</td>\n",
              "      <td>DBT-S01753</td>\n",
              "      <td>lcc</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P01481/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P01481/1.2.826...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19145</th>\n",
              "      <td>DBT-P01481</td>\n",
              "      <td>DBT-S01753</td>\n",
              "      <td>lmlo</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P01481/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P01481/1.2.826...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19146</th>\n",
              "      <td>DBT-P01481</td>\n",
              "      <td>DBT-S01753</td>\n",
              "      <td>rcc</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P01481/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P01481/1.2.826...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19147</th>\n",
              "      <td>DBT-P01481</td>\n",
              "      <td>DBT-S01753</td>\n",
              "      <td>rmlo</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P01481/01-01-2...</td>\n",
              "      <td>Breast-Cancer-Screening-DBT/DBT-P01481/1.2.826...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19148 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        PatientID    StudyUID  View  \\\n",
              "0      DBT-P00013  DBT-S00163  rmlo   \n",
              "1      DBT-P00023  DBT-S04378   lcc   \n",
              "2      DBT-P00023  DBT-S04378  lmlo   \n",
              "3      DBT-P00023  DBT-S04378   rcc   \n",
              "4      DBT-P00023  DBT-S04378  rmlo   \n",
              "...           ...         ...   ...   \n",
              "19143  DBT-P04356  DBT-S00073  rmlo   \n",
              "19144  DBT-P01481  DBT-S01753   lcc   \n",
              "19145  DBT-P01481  DBT-S01753  lmlo   \n",
              "19146  DBT-P01481  DBT-S01753   rcc   \n",
              "19147  DBT-P01481  DBT-S01753  rmlo   \n",
              "\n",
              "                                        descriptive_path  \\\n",
              "0      Breast-Cancer-Screening-DBT/DBT-P00013/01-01-2...   \n",
              "1      Breast-Cancer-Screening-DBT/DBT-P00023/01-01-2...   \n",
              "2      Breast-Cancer-Screening-DBT/DBT-P00023/01-01-2...   \n",
              "3      Breast-Cancer-Screening-DBT/DBT-P00023/01-01-2...   \n",
              "4      Breast-Cancer-Screening-DBT/DBT-P00023/01-01-2...   \n",
              "...                                                  ...   \n",
              "19143  Breast-Cancer-Screening-DBT/DBT-P04356/01-01-2...   \n",
              "19144  Breast-Cancer-Screening-DBT/DBT-P01481/01-01-2...   \n",
              "19145  Breast-Cancer-Screening-DBT/DBT-P01481/01-01-2...   \n",
              "19146  Breast-Cancer-Screening-DBT/DBT-P01481/01-01-2...   \n",
              "19147  Breast-Cancer-Screening-DBT/DBT-P01481/01-01-2...   \n",
              "\n",
              "                                            classic_path  \n",
              "0      Breast-Cancer-Screening-DBT/DBT-P00013/1.2.826...  \n",
              "1      Breast-Cancer-Screening-DBT/DBT-P00023/1.2.826...  \n",
              "2      Breast-Cancer-Screening-DBT/DBT-P00023/1.2.826...  \n",
              "3      Breast-Cancer-Screening-DBT/DBT-P00023/1.2.826...  \n",
              "4      Breast-Cancer-Screening-DBT/DBT-P00023/1.2.826...  \n",
              "...                                                  ...  \n",
              "19143  Breast-Cancer-Screening-DBT/DBT-P04356/1.2.826...  \n",
              "19144  Breast-Cancer-Screening-DBT/DBT-P01481/1.2.826...  \n",
              "19145  Breast-Cancer-Screening-DBT/DBT-P01481/1.2.826...  \n",
              "19146  Breast-Cancer-Screening-DBT/DBT-P01481/1.2.826...  \n",
              "19147  Breast-Cancer-Screening-DBT/DBT-P01481/1.2.826...  \n",
              "\n",
              "[19148 rows x 5 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(\"E:/project/BCS-DBT file-paths-train-v2.csv\")\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "lpKYdSME7ODz",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "lpKYdSME7ODz",
        "outputId": "b74652e0-0488-4b1e-a56c-5fe0f3bee428"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Patient ID: DBT-P04742  View:rmlo  Index Number:  15655\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "file_path = \"E:/project/data/manifest-1617905855234\"\n",
        "path = view_image_rand(file_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "w1LBPS61P6uU",
      "metadata": {
        "id": "w1LBPS61P6uU"
      },
      "source": [
        "# Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "AZW0ajivP4v5",
      "metadata": {
        "id": "AZW0ajivP4v5"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "import pydicom\n",
        "import numpy as np\n",
        "import cv2\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "70XgYlXcog5y",
      "metadata": {
        "id": "70XgYlXcog5y"
      },
      "source": [
        "## Helper function "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "RLJV6yGlUa5S",
      "metadata": {
        "id": "RLJV6yGlUa5S"
      },
      "outputs": [],
      "source": [
        "def get_label(row):\n",
        "  condition = ['Normal','Actionable','Benign','Cancer']\n",
        "  for con in condition:\n",
        "    if row.iloc[0][con] == 1:\n",
        "        if con == 'Normal':\n",
        "            return con\n",
        "        else:\n",
        "            return 'Cancer'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WJsolkR9olmk",
      "metadata": {
        "id": "WJsolkR9olmk"
      },
      "source": [
        "## Processing the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "IUxqCMn6QTO0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUxqCMn6QTO0",
        "outputId": "cea46b4c-d0b6-4173-ba78-84542a07bc93"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19148\n",
            "Cancer\n"
          ]
        }
      ],
      "source": [
        "labels = pd.read_csv(\"BCS-DBT labels-train-v2.csv\")\n",
        "print(len(labels))\n",
        "labels = labels.drop_duplicates(\"PatientID\")\n",
        "\n",
        "row =  row = labels[labels['PatientID'].str.contains(\"DBT-P00023\")]\n",
        "print(get_label(row))\n",
        "# # print(len(labels))\n",
        "# # # # labels.head(6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "qBGR_D8Z7rmb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "1e863c23657846e0b71fc12b7b641c8f",
            "a00c8a258c0b4bae81c120877fc676ef",
            "a4323fa036e14a139e4cf1285adac657",
            "6b172bf85384416b8952d1bab3f40ad0",
            "298fe162ff1d42d4908de8a4d5538968",
            "f34f05561c6f4a78827f366de97a2156",
            "7659ea7e3e96497da9382cedf03ef0d4",
            "df23a924c5de4b7daff2567f0917f25c",
            "341b37ec8d894113bf767f0024dbb24c",
            "bac1ab76f65a4f17aa39c26deee998bd",
            "7acc799d4f8e4016895b52b47e78165a",
            "82128a82506b4ebc8026fc20946c1910"
          ]
        },
        "id": "qBGR_D8Z7rmb",
        "outputId": "252b7e72-42a0-4abd-b30e-e9815c875fab"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "82128a82506b4ebc8026fc20946c1910",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2234 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "ROOT_PATH = Path(\"G:/My Drive/ForWin/processed\")\n",
        "df = pd.read_csv(\"BCS-DBT file-paths-train-v2.csv\")\n",
        "\n",
        "length = len(df)\n",
        "\n",
        "for index in tqdm(range(16914,length)):\n",
        "  file_path = Path(\"E:/project/data/manifest-1617905855234\")\n",
        "\n",
        "  # get the pixel array\n",
        "  view_series = df.iloc[index]\n",
        "  spec_path = view_series['descriptive_path']\n",
        "  spec_path = change_path(spec_path)\n",
        "  view = view_series[\"View\"]\n",
        "  path = file_path / spec_path\n",
        "  image = dcmread_image(fp=path, view=view)\n",
        "\n",
        "  #resizing the pixel array\n",
        "\n",
        "  dcm = image[0]/224\n",
        "  dcm_array = cv2.resize(dcm,(225,225)).astype(np.float16)\n",
        "\n",
        "  #get the label of that patient\n",
        "  patient_id = view_series['PatientID']\n",
        "  row = labels[labels['PatientID'].str.contains(patient_id)]\n",
        "  label = get_label(row)\n",
        "  patient_id = patient_id + \"_\" + view\n",
        "  \n",
        "  train_or_val = \"training\" if index < length*0.9 else \"validation\"\n",
        "\n",
        "  current_save_path = ROOT_PATH/train_or_val/str(label)\n",
        "  current_save_path.mkdir(parents = True, exist_ok = True)\n",
        "  np.save(current_save_path/patient_id,dcm_array)\n",
        "    \n",
        "  print(\"finish processing image at index {}\".format(index))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "background_execution": "on",
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "Preprocessing.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "widgets": {
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