viz_rsna_hemorrhage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import pandas as pd\n",
    "from pydicom import dcmread\n",
    "from pydicom.pixel_data_handlers.util import apply_voi_lut\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import json\n",
    "from PIL import Image\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "sns.set_context(\"paper\")\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"./\")\n",
    "\n",
    "# from dataset import SeriesDataset\n",
    "\n",
    "data_dir = \"/export/gaon1/data/jteneggi/data/rsna-intracranial-hemorrhage-detection\"\n",
    "train_dir = os.path.join(data_dir, \"stage_2_train\")\n",
    "# train_csv = os.path.join(data_dir, \"stage_2_train.csv\")\n",
    "# pytorch_train_dir = os.path.join(data_dir, \"train\")\n",
    "# pytorch_test_dir = os.path.join(data_dir, \"test\")\n",
    "\n",
    "# train_dataset = SeriesDataset(data_dir=data_dir, op=\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Found 21744 unique series.\n"
     ]
    }
   ],
   "source": [
    "with open(os.path.join(data_dir, \"raw_series.json\"), \"r\", encoding=\"utf-8\") as f:\n",
    "    sorted_series = json.load(f)\n",
    "series_ids = list(sorted_series.keys())\n",
    "\n",
    "print(f\"Found {len(sorted_series)} unique series.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'series': [['76d55d9d0', '0'], ['96d282ea9', '0'], ['7d8a7c29d', '0'], ['4d4401491', '0'], ['8f5ded0b7', '0'], ['213346b48', '0'], ['e07862283', '0'], ['c29529650', '0'], ['1bc422f0c', '0'], ['73bf91129', '0'], ['55a7874f6', '0'], ['2c2a37426', '0'], ['e0276df9e', '0'], ['1a5bac972', '0'], ['d2e7247f5', '0'], ['79273c19d', '0'], ['2d0e5725a', '0'], ['67591eae2', '0'], ['fee5c2786', '0'], ['12bf83ff1', '0'], ['bbdc941dd', '0'], ['483e6a157', '0'], ['424b93897', '0'], ['d292f6cab', '0'], ['53553ad15', '0'], ['4549ac4c6', '0'], ['14faeae26', '0'], ['1d97ba8b4', '0'], ['ff1235609', '0'], ['3818edf6c', '0'], ['cd15da2e3', '0'], ['da9a9b844', '0'], ['6b55e3e00', '0'], ['371de8f49', '0'], ['bb7aea41b', '0'], ['8db919b5c', '0'], ['f870dcfd0', '0'], ['d8a328562', '0'], ['9a319f990', '0'], ['36a144b7a', '0']], 'target': '0'}\n"
     ]
    }
   ],
   "source": [
    "print(sorted_series[series_ids[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Found 8882 series with and 12862 without hemorrhage.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Length distribution of healthy and sick series')"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "series_l = np.fromiter((len(series[\"series\"]) for _, series in sorted_series.items()), dtype=int)\n",
    "plt.hist(series_l, bins=30)\n",
    "plt.xlabel(\"Series length (images)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(f\"Length distribution of {len(sorted_series)} series\")\n",
    "plt.show()\n",
    "\n",
    "series_labels = np.fromiter((int(series[\"target\"]) for _, series in sorted_series.items()), dtype=bool)\n",
    "print(f\"Found {sum(series_labels)} series with and {len(series_labels) - sum(series_labels)} without hemorrhage.\")\n",
    "plt.hist(series_l[series_labels == 0], bins=30, color=\"g\", label=\"healthy\", alpha=.75)\n",
    "plt.hist(series_l[series_labels == 1], bins=30, color=\"r\", label=\"sick\", alpha=.75)\n",
    "plt.legend()\n",
    "plt.xlabel(\"Series length (images)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(f\"Length distribution of healthy and sick series\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[['76d55d9d0' '0']\n ['96d282ea9' '0']\n ['7d8a7c29d' '0']\n ['4d4401491' '0']\n ['8f5ded0b7' '0']\n ['213346b48' '0']\n ['e07862283' '0']\n ['c29529650' '0']\n ['1bc422f0c' '0']\n ['73bf91129' '0']\n ['55a7874f6' '0']\n ['2c2a37426' '0']\n ['e0276df9e' '0']\n ['1a5bac972' '0']\n ['d2e7247f5' '0']\n ['79273c19d' '0']\n ['2d0e5725a' '0']\n ['67591eae2' '0']\n ['fee5c2786' '0']\n ['12bf83ff1' '0']\n ['bbdc941dd' '0']\n ['483e6a157' '0']\n ['424b93897' '0']\n ['d292f6cab' '0']\n ['53553ad15' '0']\n ['4549ac4c6' '0']\n ['14faeae26' '0']\n ['1d97ba8b4' '0']\n ['ff1235609' '0']\n ['3818edf6c' '0']\n ['cd15da2e3' '0']\n ['da9a9b844' '0']\n ['6b55e3e00' '0']\n ['371de8f49' '0']\n ['bb7aea41b' '0']\n ['8db919b5c' '0']\n ['f870dcfd0' '0']\n ['d8a328562' '0']\n ['9a319f990' '0']\n ['36a144b7a' '0']]\nSeries target: 0\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/export/gaon1/data/jteneggi/data/rsna-intracranial-hemorrhage-detection/stage_2_train/76d55d9d0.npy'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-1569d69f06e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mimg_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"{image}.npy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"gray\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Slice: {i} | Label: {label}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/cuda10/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    414\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m             \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menter_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/export/gaon1/data/jteneggi/data/rsna-intracranial-hemorrhage-detection/stage_2_train/76d55d9d0.npy'"
     ]
    }
   ],
   "source": [
    "# series, target, labels = train_dataset[np.random.choice(range(0, len(train_dataset)))]\n",
    "series = sorted_series[series_ids[0]]\n",
    "series_images = np.array(series[\"series\"])\n",
    "print(series_images)\n",
    "series_target = int(series[\"target\"])\n",
    "print(f\"Series target: {series_target}\")\n",
    "\n",
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "\n",
    "for i, (image, label) in enumerate(series_data):\n",
    "    img_path = os.path.join(train_dir, f\"ID_{image}.npy\")\n",
    "    img = np.load(img_path)\n",
    "    plt.imshow(img, cmap=\"gray\")\n",
    "    plt.title(f\"Slice: {i} | Label: {label}\")\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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