{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import sys\n", "import os\n", "sys.path.append('/ubc/ece/home/ra/grads/siyi/Research/skin_lesion_segmentation/MDViT/')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2594\n" ] } ], "source": [ "'''isic2018'''\n", "# Note ISIC original meta file is in this code reporsitory, MDViT/Datasets/isic2018_id.csv\n", "data_folder = '/bigdata/siyiplace/data/skin_lesion'\n", "isic2018_meta = 'isic2018_id.csv'\n", "df = pd.read_csv(isic2018_meta)\n", "print(len(df))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "diagnosis_list = []\n", "ID_list = []\n", "for i in range(len(df)):\n", " diagnosis_list.append(df.iloc[i]['Class'].capitalize())\n", " ID_list.append(df.iloc[i]['ID'].split('_')[1])\n", "df['diagnosis'] = diagnosis_list\n", "df['dataset'] = ['isic2018']*len(df)\n", "df['ID'] = ID_list\n", "df['diagnosis_id'] = df['diagnosis'].astype('category').cat.codes" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDClassSplitWHpigment_networknegative_networkstreaksmilia_like_cystglobulesalldiagnosisdatasetdiagnosis_id
00000164melanomatrain638959100001Melanomaisic20180
10001148melanomatrain23043072100001Melanomaisic20180
20013258melanomatrain20003008100001Melanomaisic20180
30014189melanomatrain28484288100102Melanomaisic20180
40013457melanomatrain28484288000101Melanomaisic20180
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25890014503seborrheic_keratosistest28484288000101Seborrheic_keratosisisic20182
25900012358seborrheic_keratosistest20003008000000Seborrheic_keratosisisic20182
25910013333seborrheic_keratosistest28484288000000Seborrheic_keratosisisic20182
25920012693seborrheic_keratosistest20003008000000Seborrheic_keratosisisic20182
25930014580seborrheic_keratosistest19362592000101Seborrheic_keratosisisic20182
\n", "

2594 rows × 14 columns

\n", "
" ], "text/plain": [ " ID Class Split W H pigment_network \\\n", "0 0000164 melanoma train 638 959 1 \n", "1 0001148 melanoma train 2304 3072 1 \n", "2 0013258 melanoma train 2000 3008 1 \n", "3 0014189 melanoma train 2848 4288 1 \n", "4 0013457 melanoma train 2848 4288 0 \n", "... ... ... ... ... ... ... \n", "2589 0014503 seborrheic_keratosis test 2848 4288 0 \n", "2590 0012358 seborrheic_keratosis test 2000 3008 0 \n", "2591 0013333 seborrheic_keratosis test 2848 4288 0 \n", "2592 0012693 seborrheic_keratosis test 2000 3008 0 \n", "2593 0014580 seborrheic_keratosis test 1936 2592 0 \n", "\n", " negative_network streaks milia_like_cyst globules all \\\n", "0 0 0 0 0 1 \n", "1 0 0 0 0 1 \n", "2 0 0 0 0 1 \n", "3 0 0 1 0 2 \n", "4 0 0 1 0 1 \n", "... ... ... ... ... ... \n", "2589 0 0 1 0 1 \n", "2590 0 0 0 0 0 \n", "2591 0 0 0 0 0 \n", "2592 0 0 0 0 0 \n", "2593 0 0 1 0 1 \n", "\n", " diagnosis dataset diagnosis_id \n", "0 Melanoma isic2018 0 \n", "1 Melanoma isic2018 0 \n", "2 Melanoma isic2018 0 \n", "3 Melanoma isic2018 0 \n", "4 Melanoma isic2018 0 \n", "... ... ... ... \n", "2589 Seborrheic_keratosis isic2018 2 \n", "2590 Seborrheic_keratosis isic2018 2 \n", "2591 Seborrheic_keratosis isic2018 2 \n", "2592 Seborrheic_keratosis isic2018 2 \n", "2593 Seborrheic_keratosis isic2018 2 \n", "\n", "[2594 rows x 14 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "df.to_csv(data_folder+'/isic2018/meta_isic2018.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200\n" ] } ], "source": [ "'''PH2'''\n", "data_folder = '/bigdata/siyiplace/data/skin_lesion'\n", "PH2_meta = data_folder+'/PH2_rawdata/PH2Dataset/PH2_dataset.xlsx'\n", "df = pd.read_excel(PH2_meta, skiprows=range(12))\n", "print(len(df))\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "diagnosis_list = []\n", "for i in range(len(df)):\n", " row = df.iloc[i]\n", " for diag_name in ['Common Nevus', 'Atypical Nevus', 'Melanoma']:\n", " if row[diag_name] == 'X':\n", " diagnosis_list.append(diag_name)\n", " break\n", " else:\n", " print(i)\n", "df['diagnosis'] = diagnosis_list\n", "df['dataset'] = ['PH2']*len(df)\n", "df['ID']=df['Image Name']\n", "df['diagnosis_id'] = df['diagnosis'].astype('category').cat.codes" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Image NameHistological DiagnosisCommon NevusAtypical NevusMelanomaAsymmetry\\n(0/1/2)Pigment Network\\n(AT/T)Dots/Globules\\n(A/AT/T)Streaks\\n(A/P)Regression Areas\\n(A/P)...WhiteRedLight-BrownDark-BrownBlue-GrayBlackdiagnosisdatasetIDdiagnosis_id
0IMD003NaNXNaNNaN0TAAA...NaNNaNNaNXNaNNaNCommon NevusPH2IMD0031
1IMD009NaNXNaNNaN0TAAA...NaNNaNXNaNNaNNaNCommon NevusPH2IMD0091
2IMD016NaNXNaNNaN0TTAA...NaNNaNXXNaNNaNCommon NevusPH2IMD0161
3IMD022NaNXNaNNaN0TAAA...NaNNaNXNaNNaNNaNCommon NevusPH2IMD0221
4IMD024NaNXNaNNaN0TAAA...NaNNaNXXNaNNaNCommon NevusPH2IMD0241
..................................................................
195IMD424MelanomaNaNNaNX2ATATPA...NaNNaNXXXXMelanomaPH2IMD4242
196IMD425MelanomaNaNNaNX2ATATAP...NaNNaNXXXXMelanomaPH2IMD4252
197IMD426MelanomaNaNNaNX2ATAAP...NaNNaNXXNaNNaNMelanomaPH2IMD4262
198IMD429NaNNaNNaNX0ATAPA...NaNNaNXXXXMelanomaPH2IMD4292
199IMD435Lentigo MalignaNaNNaNX2ATAAA...NaNNaNXXXXMelanomaPH2IMD4352
\n", "

200 rows × 21 columns

\n", "
" ], "text/plain": [ " Image Name Histological Diagnosis Common Nevus Atypical Nevus Melanoma \\\n", "0 IMD003 NaN X NaN NaN \n", "1 IMD009 NaN X NaN NaN \n", "2 IMD016 NaN X NaN NaN \n", "3 IMD022 NaN X NaN NaN \n", "4 IMD024 NaN X NaN NaN \n", ".. ... ... ... ... ... \n", "195 IMD424 Melanoma NaN NaN X \n", "196 IMD425 Melanoma NaN NaN X \n", "197 IMD426 Melanoma NaN NaN X \n", "198 IMD429 NaN NaN NaN X \n", "199 IMD435 Lentigo Maligna NaN NaN X \n", "\n", " Asymmetry\\n(0/1/2) Pigment Network\\n(AT/T) Dots/Globules\\n(A/AT/T) \\\n", "0 0 T A \n", "1 0 T A \n", "2 0 T T \n", "3 0 T A \n", "4 0 T A \n", ".. ... ... ... \n", "195 2 AT AT \n", "196 2 AT AT \n", "197 2 AT A \n", "198 0 AT A \n", "199 2 AT A \n", "\n", " Streaks\\n(A/P) Regression Areas\\n(A/P) ... White Red Light-Brown \\\n", "0 A A ... NaN NaN NaN \n", "1 A A ... NaN NaN X \n", "2 A A ... NaN NaN X \n", "3 A A ... NaN NaN X \n", "4 A A ... NaN NaN X \n", ".. ... ... ... ... ... ... \n", "195 P A ... NaN NaN X \n", "196 A P ... NaN NaN X \n", "197 A P ... NaN NaN X \n", "198 P A ... NaN NaN X \n", "199 A A ... NaN NaN X \n", "\n", " Dark-Brown Blue-Gray Black diagnosis dataset ID diagnosis_id \n", "0 X NaN NaN Common Nevus PH2 IMD003 1 \n", "1 NaN NaN NaN Common Nevus PH2 IMD009 1 \n", "2 X NaN NaN Common Nevus PH2 IMD016 1 \n", "3 NaN NaN NaN Common Nevus PH2 IMD022 1 \n", "4 X NaN NaN Common Nevus PH2 IMD024 1 \n", ".. ... ... ... ... ... ... ... \n", "195 X X X Melanoma PH2 IMD424 2 \n", "196 X X X Melanoma PH2 IMD425 2 \n", "197 X NaN NaN Melanoma PH2 IMD426 2 \n", "198 X X X Melanoma PH2 IMD429 2 \n", "199 X X X Melanoma PH2 IMD435 2 \n", "\n", "[200 rows x 21 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "df.to_csv(data_folder+'/PH2/meta_PH2.csv',index=False)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1212\n" ] } ], "source": [ "'''DMF'''\n", "data_folder = '/bigdata/siyiplace/data/skin_lesion'\n", "DMF_meta = data_folder+'/DMF_origin/meta.csv'\n", "df = pd.read_csv(DMF_meta)\n", "print(len(df))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pathlesion_idori_dxdxdx_idxdom
0images/D204a/D204a.png1BCCBasal cell carcinoma2dmf
1images/D226b/D226b.png3MLMelanocytic nevi1dmf
2images/D226c/D226c.png4MLMelanocytic nevi1dmf
3images/D227/D227.png5MLMelanocytic nevi1dmf
4images/D231/D231.png6AKActinic keratoses3dmf
.....................
1207images/D649a/D649a.png1296IECActinic keratoses3dmf
1208images/G40a/G40a.png1297IECActinic keratoses3dmf
1209images/P100/P100.png1298IECActinic keratoses3dmf
1210images/T239/T239.png1299IECActinic keratoses3dmf
1211images/T39/T39.png1300IECActinic keratoses3dmf
\n", "

1212 rows × 6 columns

\n", "
" ], "text/plain": [ " path lesion_id ori_dx dx dx_idx \\\n", "0 images/D204a/D204a.png 1 BCC Basal cell carcinoma 2 \n", "1 images/D226b/D226b.png 3 ML Melanocytic nevi 1 \n", "2 images/D226c/D226c.png 4 ML Melanocytic nevi 1 \n", "3 images/D227/D227.png 5 ML Melanocytic nevi 1 \n", "4 images/D231/D231.png 6 AK Actinic keratoses 3 \n", "... ... ... ... ... ... \n", "1207 images/D649a/D649a.png 1296 IEC Actinic keratoses 3 \n", "1208 images/G40a/G40a.png 1297 IEC Actinic keratoses 3 \n", "1209 images/P100/P100.png 1298 IEC Actinic keratoses 3 \n", "1210 images/T239/T239.png 1299 IEC Actinic keratoses 3 \n", "1211 images/T39/T39.png 1300 IEC Actinic keratoses 3 \n", "\n", " dom \n", "0 dmf \n", "1 dmf \n", "2 dmf \n", "3 dmf \n", "4 dmf \n", "... ... \n", "1207 dmf \n", "1208 dmf \n", "1209 dmf \n", "1210 dmf \n", "1211 dmf \n", "\n", "[1212 rows x 6 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "df['dataset']=['DMF']*len(df)\n", "diagnosis_list = []\n", "ID_list = []\n", "for i in range(len(df)):\n", " path = df.iloc[i]['path']\n", " ID_list.append(path.split('/')[1])\n", " diagnosis_list.append(df.iloc[i]['dx'].title())\n", "df['ID']=ID_list\n", "df['diagnosis'] = diagnosis_list\n", "df['diagnosis_id'] = df['diagnosis'].astype('category').cat.codes" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pathlesion_idori_dxdxdx_idxdomdatasetIDdiagnosisdiagnosis_id
0images/D204a/D204a.png1BCCBasal cell carcinoma2dmfDMFD204aBasal Cell Carcinoma1
1images/D226b/D226b.png3MLMelanocytic nevi1dmfDMFD226bMelanocytic Nevi4
2images/D226c/D226c.png4MLMelanocytic nevi1dmfDMFD226cMelanocytic Nevi4
3images/D227/D227.png5MLMelanocytic nevi1dmfDMFD227Melanocytic Nevi4
4images/D231/D231.png6AKActinic keratoses3dmfDMFD231Actinic Keratoses0
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1207images/D649a/D649a.png1296IECActinic keratoses3dmfDMFD649aActinic Keratoses0
1208images/G40a/G40a.png1297IECActinic keratoses3dmfDMFG40aActinic Keratoses0
1209images/P100/P100.png1298IECActinic keratoses3dmfDMFP100Actinic Keratoses0
1210images/T239/T239.png1299IECActinic keratoses3dmfDMFT239Actinic Keratoses0
1211images/T39/T39.png1300IECActinic keratoses3dmfDMFT39Actinic Keratoses0
\n", "

1212 rows × 10 columns

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" ], "text/plain": [ " path lesion_id ori_dx dx dx_idx \\\n", "0 images/D204a/D204a.png 1 BCC Basal cell carcinoma 2 \n", "1 images/D226b/D226b.png 3 ML Melanocytic nevi 1 \n", "2 images/D226c/D226c.png 4 ML Melanocytic nevi 1 \n", "3 images/D227/D227.png 5 ML Melanocytic nevi 1 \n", "4 images/D231/D231.png 6 AK Actinic keratoses 3 \n", "... ... ... ... ... ... \n", "1207 images/D649a/D649a.png 1296 IEC Actinic keratoses 3 \n", "1208 images/G40a/G40a.png 1297 IEC Actinic keratoses 3 \n", "1209 images/P100/P100.png 1298 IEC Actinic keratoses 3 \n", "1210 images/T239/T239.png 1299 IEC Actinic keratoses 3 \n", "1211 images/T39/T39.png 1300 IEC Actinic keratoses 3 \n", "\n", " dom dataset ID diagnosis diagnosis_id \n", "0 dmf DMF D204a Basal Cell Carcinoma 1 \n", "1 dmf DMF D226b Melanocytic Nevi 4 \n", "2 dmf DMF D226c Melanocytic Nevi 4 \n", "3 dmf DMF D227 Melanocytic Nevi 4 \n", "4 dmf DMF D231 Actinic Keratoses 0 \n", "... ... ... ... ... ... \n", "1207 dmf DMF D649a Actinic Keratoses 0 \n", "1208 dmf DMF G40a Actinic Keratoses 0 \n", "1209 dmf DMF P100 Actinic Keratoses 0 \n", "1210 dmf DMF T239 Actinic Keratoses 0 \n", "1211 dmf DMF T39 Actinic Keratoses 0 \n", "\n", "[1212 rows x 10 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "df.to_csv(data_folder+'/DMF/meta_DMF.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "'''SKD\n", "create meta file'''\n", "data_folder = '/bigdata/siyiplace/data/skin_lesion'\n", "melanoma_folder = data_folder+'/skin_cancer_detection_origin/skin_image_data_set-1/Skin Image Data Set-1/skin_data/melanoma'\n", "benign_folder = data_folder+'/skin_cancer_detection_origin/skin_image_data_set-2/Skin Image Data Set-2/skin_data/notmelanoma'\n", "image_name_list = []\n", "diagnosis_list = []\n", "\n", "for dataset_name in ['dermis', 'dermquest']:\n", " path_list = os.listdir('{}/{}'.format(melanoma_folder, dataset_name))\n", " for path in path_list:\n", " if path[-4:] == '.jpg':\n", " image_name = dataset_name+'_'+path[:-4]\n", " image_name_list.append(image_name)\n", "\n", "diagnosis_list.extend(['Melanoma']*len(image_name_list))\n", "\n", "count = 0\n", "for dataset_name in ['dermis', 'dermquest']:\n", " path_list = os.listdir('{}/{}'.format(benign_folder, dataset_name))\n", "\n", " for path in path_list:\n", " if path[-4:] == '.jpg':\n", " image_name = dataset_name+'_'+path[:-4]\n", " image_name_list.append(image_name)\n", " count += 1\n", "\n", "diagnosis_list.extend(['Benign']*count)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "206\n", "206\n" ] } ], "source": [ "print(len(diagnosis_list))\n", "print(len(image_name_list))\n", "SKD_meta_path = data_folder+'/SKD/meta_SKD.csv'\n", "df = pd.DataFrame({\n", " 'dataset': ['SKD']*len(diagnosis_list),\n", " 'ID': image_name_list,\n", " 'diagnosis': diagnosis_list,\n", "})\n", "df.to_csv(SKD_meta_path, index=False)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "SKD_meta_path = data_folder+'/SKD/meta_SKD.csv'\n", "SKD_meta = pd.read_csv(SKD_meta_path)\n", "# SKD_meta" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "df['diagnosis_id'] = df['diagnosis'].astype('category').cat.codes" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datasetIDdiagnosisdiagnosis_id
0SKDdermis_AMM1_origMelanoma1
1SKDdermis_LMM1_origMelanoma1
2SKDdermis_LMM2_origMelanoma1
3SKDdermis_LMM4_origMelanoma1
4SKDdermis_LMM5_origMelanoma1
...............
201SKDdermquest_D6_origBenign0
202SKDdermquest_D8_2_origBenign0
203SKDdermquest_D8_origBenign0
204SKDdermquest_D9_2_origBenign0
205SKDdermquest_D9_origBenign0
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206 rows × 4 columns

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" ], "text/plain": [ " dataset ID diagnosis diagnosis_id\n", "0 SKD dermis_AMM1_orig Melanoma 1\n", "1 SKD dermis_LMM1_orig Melanoma 1\n", "2 SKD dermis_LMM2_orig Melanoma 1\n", "3 SKD dermis_LMM4_orig Melanoma 1\n", "4 SKD dermis_LMM5_orig Melanoma 1\n", ".. ... ... ... ...\n", "201 SKD dermquest_D6_orig Benign 0\n", "202 SKD dermquest_D8_2_orig Benign 0\n", "203 SKD dermquest_D8_orig Benign 0\n", "204 SKD dermquest_D9_2_orig Benign 0\n", "205 SKD dermquest_D9_orig Benign 0\n", "\n", "[206 rows x 4 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "df.to_csv(SKD_meta_path, index=False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.12 ('skinlesion')", "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": "3.8.12" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "d7d995bc54c9aaecfe64e6668cd10ce18771211a7298b34b6f0fab6698bea7d7" } } }, "nbformat": 4, "nbformat_minor": 2 }