{
  "source": "Meta Kaggle Kernels.csv",
  "source_snapshot": {
    "source_mtime": 1790828993000000000,
    "source_date": "2026-10-01T04:29:53Z"
  },
  "items": [
    {
      "ref": "wcukierski/example-metric-implementation",
      "kernel_id": "541777",
      "votes": 239
    },
    {
      "ref": "ahassaine/pure-image-processing-lb-0-274",
      "kernel_id": "563515",
      "votes": 76
    },
    {
      "ref": "kmader/optimizing-ali-s-segmentation",
      "kernel_id": "574768",
      "votes": 24
    },
    {
      "ref": "kmader/optimizing-computer-vision-segmentation",
      "kernel_id": "573457",
      "votes": 48
    },
    {
      "ref": "kmader/keras-linknet",
      "kernel_id": "595418",
      "votes": 69
    },
    {
      "ref": "kmader/keras-rcnn-based-overview-wip",
      "kernel_id": "588019",
      "votes": 176
    },
    {
      "ref": "kmader/normalizing-brightfield-stained-and-fluorescence",
      "kernel_id": "543578",
      "votes": 159
    },
    {
      "ref": "kmader/nuclei-overview-to-submission",
      "kernel_id": "542047",
      "votes": 672
    },
    {
      "ref": "kmader/dsb2018-visualizing-u-net-with-conx",
      "kernel_id": "813080",
      "votes": 28
    },
    {
      "ref": "panchacookie/notebookfb3bcbf35c",
      "kernel_id": "586434",
      "votes": 0
    },
    {
      "ref": "sfhousewife/keras-u-net-starter-lb-0-277",
      "kernel_id": "702343",
      "votes": 2
    },
    {
      "ref": "etheleon/exploratory-analysis-image-stats",
      "kernel_id": "542082",
      "votes": 73
    },
    {
      "ref": "devinanzelmo/looking-at-fpn",
      "kernel_id": "575835",
      "votes": 5
    },
    {
      "ref": "jakubczakon/improve-the-dataset-with-non-nuclei-images",
      "kernel_id": "816372",
      "votes": 5
    },
    {
      "ref": "jakubczakon/network-for-size-estimation-rescale",
      "kernel_id": "817060",
      "votes": 2
    },
    {
      "ref": "jakubczakon/morphological-postprocessing-on-unet-lb-0-429",
      "kernel_id": "716486",
      "votes": 170
    },
    {
      "ref": "psaadbye/keras-u-net-starter-lb-0-277",
      "kernel_id": "552504",
      "votes": 0
    },
    {
      "ref": "bguberfain/naive-ellipsis-analysis",
      "kernel_id": "548346",
      "votes": 7
    },
    {
      "ref": "nitinaggarwal008/teaching-notebook-for-total-imaging-newbies",
      "kernel_id": "757585",
      "votes": 4
    },
    {
      "ref": "transwarp/combine-mask-and-label-mask-boundary",
      "kernel_id": "564316",
      "votes": 4
    },
    {
      "ref": "keegil/keras-u-net-starter-lb-0-277",
      "kernel_id": "542039",
      "votes": 3501
    },
    {
      "ref": "voglinio/separating-nuclei-masks-using-convexity-defects",
      "kernel_id": "755150",
      "votes": 35
    },
    {
      "ref": "voglinio/external-h-e-data-with-mask-annotations",
      "kernel_id": "731708",
      "votes": 133
    },
    {
      "ref": "fabsta/nuclei-segmentation-tensorflow-u-net-0-327-027909",
      "kernel_id": "626342",
      "votes": 27
    },
    {
      "ref": "ori226/data-augmentation-with-elastic-deformations",
      "kernel_id": "581925",
      "votes": 40
    },
    {
      "ref": "tangchuanxin/keras-u-net-starter-lb-0-233",
      "kernel_id": "543635",
      "votes": 1
    },
    {
      "ref": "vanausloos/from-keras-lb-0-262",
      "kernel_id": "636891",
      "votes": 5
    },
    {
      "ref": "vanausloos/how-to-read-images-in-python",
      "kernel_id": "634070",
      "votes": 68
    },
    {
      "ref": "amineteffal/helper-functions",
      "kernel_id": "722577",
      "votes": 0
    },
    {
      "ref": "jerrythomas/issues-in-images",
      "kernel_id": "597801",
      "votes": 6
    },
    {
      "ref": "jerrythomas/exploratory-analysis",
      "kernel_id": "589957",
      "votes": 99
    },
    {
      "ref": "thomasjpfan/ap-metric",
      "kernel_id": "620393",
      "votes": 3
    },
    {
      "ref": "tarunad/morphological-postprocessing-on-unet-lb-0-429",
      "kernel_id": "826308",
      "votes": 0
    },
    {
      "ref": "stainsby/fast-tested-rle-and-input-routines",
      "kernel_id": "563325",
      "votes": 57
    },
    {
      "ref": "drgilermo/kernel4bfa79d30f",
      "kernel_id": "702362",
      "votes": 0
    },
    {
      "ref": "robertkag/rle-to-mask-converter",
      "kernel_id": "850669",
      "votes": 59
    },
    {
      "ref": "cloudfall/pytorch-tutorials-on-dsb2018",
      "kernel_id": "578335",
      "votes": 108
    },
    {
      "ref": "babajaguska/resize-images-nuclei-segmentation",
      "kernel_id": "700900",
      "votes": 7
    },
    {
      "ref": "babajaguska/getting-to-know-the-data-nuclei-segmentation",
      "kernel_id": "695618",
      "votes": 38
    },
    {
      "ref": "ramzes2/distribution-of-nuclei-sizes",
      "kernel_id": "547842",
      "votes": 21
    },
    {
      "ref": "ramzes2/exploring-the-images-and-their-nuclei-mask-contous",
      "kernel_id": "542157",
      "votes": 13
    },
    {
      "ref": "danieleewww/cpmp-s-unet-solution",
      "kernel_id": "881487",
      "votes": 3
    },
    {
      "ref": "xin1q1q1211/testing-unet-latest-keras-and-tf",
      "kernel_id": "553606",
      "votes": 3
    },
    {
      "ref": "sjoly123/kerasunet-281",
      "kernel_id": "547738",
      "votes": 74
    },
    {
      "ref": "amitkumarjaiswal/image-segmentation",
      "kernel_id": "735962",
      "votes": 21
    },
    {
      "ref": "jiaxinyu/2018dsb",
      "kernel_id": "884254",
      "votes": 0
    },
    {
      "ref": "infernop/object-detection-techniques",
      "kernel_id": "723560",
      "votes": 130
    },
    {
      "ref": "bostjanm/overlapping-objects-separation-method",
      "kernel_id": "670256",
      "votes": 52
    },
    {
      "ref": "zavodrobotov/best-public-notebook-kernel",
      "kernel_id": "762692",
      "votes": 100
    },
    {
      "ref": "rexhaif/morphological-postprocessing-on-unet-lb-0-429",
      "kernel_id": "817631",
      "votes": 23
    },
    {
      "ref": "eggsinabasket/drawing-boxes",
      "kernel_id": "726133",
      "votes": 1
    },
    {
      "ref": "leogan/deformable-covolutions-in-the-autoencoder",
      "kernel_id": "882085",
      "votes": 4
    },
    {
      "ref": "jihyeseo/segmented-nuclei-images",
      "kernel_id": "958631",
      "votes": 1
    },
    {
      "ref": "lzhang57/nuclei-masks-eda",
      "kernel_id": "542170",
      "votes": 16
    },
    {
      "ref": "nhargan/defining-microscopy-type",
      "kernel_id": "555293",
      "votes": 31
    },
    {
      "ref": "mosnoiion/u-net-vgg16-open-for-team-merge-lb-0-319",
      "kernel_id": "809322",
      "votes": 10
    },
    {
      "ref": "pedrodiamel/vizualization-dataset-data-science-bowl",
      "kernel_id": "719861",
      "votes": 2
    },
    {
      "ref": "mingruimingrui/moving-on-to-localization-patch-wise-neuclei-eda",
      "kernel_id": "624862",
      "votes": 1
    },
    {
      "ref": "mauddib/data-science-bowl-tutorial-using-cnn-tensorflow",
      "kernel_id": "738963",
      "votes": 50
    },
    {
      "ref": "jeremytjordan/exploration-of-u-net-architectures",
      "kernel_id": "795597",
      "votes": 10
    },
    {
      "ref": "shiraz14/r-script-for-quick-easy-file-copying-transfer",
      "kernel_id": "850214",
      "votes": 0
    },
    {
      "ref": "mpware/stage1-eda-microscope-image-types-clustering",
      "kernel_id": "575945",
      "votes": 145
    },
    {
      "ref": "mpware/ensembling-on-instance-segmentation-lb-0-419",
      "kernel_id": "751175",
      "votes": 365
    },
    {
      "ref": "gaborvecsei/basic-pure-computer-vision-segmentation-lb-0-229",
      "kernel_id": "542083",
      "votes": 176
    },
    {
      "ref": "gaborvecsei/unsupervised-segmentation-with-type-separation",
      "kernel_id": "562448",
      "votes": 8
    },
    {
      "ref": "thenickname/annotation-tool",
      "kernel_id": "852124",
      "votes": 0
    },
    {
      "ref": "bonlime/train-test-image-mosaic",
      "kernel_id": "707285",
      "votes": 129
    },
    {
      "ref": "stevessschen/notebook6e9b0fe522",
      "kernel_id": "242028",
      "votes": 1
    },
    {
      "ref": "reddisanjeevkumar/fork-of-kernel-lb-0-3029",
      "kernel_id": "711319",
      "votes": 0
    },
    {
      "ref": "reddisanjeevkumar/kernel-lb-0-3029",
      "kernel_id": "705026",
      "votes": 3
    },
    {
      "ref": "davidretana/simple-data-science-bowl-2018-exploration",
      "kernel_id": "549179",
      "votes": 26
    },
    {
      "ref": "aglotero/another-iou-metric",
      "kernel_id": "559603",
      "votes": 113
    },
    {
      "ref": "vijaybj/basic-u-net-using-tensorflow",
      "kernel_id": "549434",
      "votes": 183
    },
    {
      "ref": "vijaybj/yolo-tensorflow-2-from-scratch",
      "kernel_id": "778287",
      "votes": 243
    },
    {
      "ref": "jruizvar/otsu-thresholding-segmentation",
      "kernel_id": "579005",
      "votes": 38
    },
    {
      "ref": "takuok/keras-generator-starter-lb-0-326",
      "kernel_id": "548707",
      "votes": 165
    },
    {
      "ref": "grybeck/keras-u-net-starter-lb-0-277",
      "kernel_id": "555244",
      "votes": 0
    },
    {
      "ref": "matthewmasters/flag-masks-with-no-area",
      "kernel_id": "663382",
      "votes": 2
    },
    {
      "ref": "zhujf553/end2end-stage1-score-computer",
      "kernel_id": "874196",
      "votes": 21
    },
    {
      "ref": "pudae81/data-visualization-and-analysis",
      "kernel_id": "546920",
      "votes": 90
    },
    {
      "ref": "asindico/bowl-of-nuclei",
      "kernel_id": "554349",
      "votes": 36
    },
    {
      "ref": "jaggaj/exploratory-notebook-with-thresholding",
      "kernel_id": "629860",
      "votes": 1
    },
    {
      "ref": "mviterson/segmentation-using-r-keras-u-net-lb-0-294",
      "kernel_id": "727857",
      "votes": 50
    },
    {
      "ref": "vannak/uniform-downsampling-with-keras",
      "kernel_id": "822082",
      "votes": 0
    },
    {
      "ref": "wt2ding/keras-u-net-starter-lb-0-277",
      "kernel_id": "714517",
      "votes": 2
    },
    {
      "ref": "satoshiterasaki/separate-object-may-your-lb-0-02",
      "kernel_id": "736642",
      "votes": 0
    },
    {
      "ref": "shenmbsw/data-augmentation-and-tensorflow-u-net",
      "kernel_id": "545675",
      "votes": 292
    },
    {
      "ref": "akshayt19nayak/getting-started-image-processing-basics",
      "kernel_id": "661851",
      "votes": 323
    },
    {
      "ref": "ponchon/mask-aggregator-and-directory-processor",
      "kernel_id": "543486",
      "votes": 3
    },
    {
      "ref": "killthekitten/assemble-mosaics",
      "kernel_id": "719205",
      "votes": 10
    },
    {
      "ref": "osciiart/cv-score-calculation-takes-5-min-in-kernel",
      "kernel_id": "631462",
      "votes": 7
    },
    {
      "ref": "byrachonok/find-the-nuclei-in-divergent-medical-images",
      "kernel_id": "543879",
      "votes": 8
    },
    {
      "ref": "jackvial/exploratory-data-analysis-dsb-2018",
      "kernel_id": "684180",
      "votes": 48
    },
    {
      "ref": "pliptor/grouping-by-color-and-difficulty-with-k-means",
      "kernel_id": "735571",
      "votes": 2
    },
    {
      "ref": "nikhilroxtomar/nuclei-overview-to-submission",
      "kernel_id": "762788",
      "votes": 3
    },
    {
      "ref": "ilyamich/creating-unified-mask-with-borders-for-train",
      "kernel_id": "741148",
      "votes": 1
    },
    {
      "ref": "vaslav/keras-u-net-segmenting-nuclei",
      "kernel_id": "568512",
      "votes": 2
    },
    {
      "ref": "enerrio/data-exploration-distribution-of-image-sizes",
      "kernel_id": "579113",
      "votes": 15
    },
    {
      "ref": "ravivadapalli/exploring-the-images-and-their-nuclei-masks",
      "kernel_id": "541987",
      "votes": 44
    },
    {
      "ref": "appsyoon/keras-u-net-exploration-notebook",
      "kernel_id": "813290",
      "votes": 11
    },
    {
      "ref": "devinshanahan/nuclei-data-visualization",
      "kernel_id": "553751",
      "votes": 5
    },
    {
      "ref": "ainilaha/data-exploratory-analysis-in-r",
      "kernel_id": "738876",
      "votes": 4
    },
    {
      "ref": "tejasvedagiri/help-me-i-dont-know-how-make-cnn-for-this",
      "kernel_id": "550237",
      "votes": 0
    },
    {
      "ref": "mrtzmilani/snmf-stain-seperation",
      "kernel_id": "713825",
      "votes": 3
    },
    {
      "ref": "c0conuts/u-net-data-augmentation-lb-0-336",
      "kernel_id": "584704",
      "votes": 5
    },
    {
      "ref": "c0conuts/unet-imagedatagenerator-lb-0-336",
      "kernel_id": "584888",
      "votes": 198
    },
    {
      "ref": "stkbailey/step-by-step-explanation-of-scoring-metric",
      "kernel_id": "744563",
      "votes": 193
    },
    {
      "ref": "stkbailey/functions-for-plotting-mask-overlays",
      "kernel_id": "542554",
      "votes": 0
    },
    {
      "ref": "stkbailey/teaching-notebook-for-total-imaging-newbies",
      "kernel_id": "578486",
      "votes": 1115
    },
    {
      "ref": "bluejay9676/vanila-u-net",
      "kernel_id": "834364",
      "votes": 2
    },
    {
      "ref": "iitrsamrat/patch-data-generator-from-original-img",
      "kernel_id": "809713",
      "votes": 14
    },
    {
      "ref": "iitrsamrat/unet-based-optimized-model-lb-347",
      "kernel_id": "766615",
      "votes": 10
    },
    {
      "ref": "aviwolfson/adding-augmentation-to-keras-u-net-starter",
      "kernel_id": "581146",
      "votes": 8
    },
    {
      "ref": "aviwolfson/kernel8b99932b88",
      "kernel_id": "584220",
      "votes": 0
    },
    {
      "ref": "coni57/multi-images-u-net",
      "kernel_id": "589506",
      "votes": 0
    },
    {
      "ref": "maffydub/nuclei-visualization",
      "kernel_id": "592219",
      "votes": 6
    },
    {
      "ref": "miklgr500/data-analysis",
      "kernel_id": "700149",
      "votes": 11
    },
    {
      "ref": "raoulma/nuclei-dsb-2018-tensorflow-u-net-score-0-352",
      "kernel_id": "562100",
      "votes": 450
    },
    {
      "ref": "tdelforge/final-sub",
      "kernel_id": "743111",
      "votes": 0
    },
    {
      "ref": "arpandhatt/data-augmenter",
      "kernel_id": "545471",
      "votes": 26
    },
    {
      "ref": "paultimothymooney/identification-and-segmentation-of-nuclei-in-cells",
      "kernel_id": "552640",
      "votes": 232
    },
    {
      "ref": "javidimail/nuclei-detection-with-opencv",
      "kernel_id": "544133",
      "votes": 48
    },
    {
      "ref": "ishootlaser/scoring-metric-for-stage-1-prediction",
      "kernel_id": "857889",
      "votes": 8
    },
    {
      "ref": "gattacacat/read-and-display-images",
      "kernel_id": "565491",
      "votes": 7
    },
    {
      "ref": "ryan211314/an-knn-classification-to-boost-u-net-lb",
      "kernel_id": "738890",
      "votes": 5
    },
    {
      "ref": "temibabs/boundary-finding",
      "kernel_id": "593757",
      "votes": 5
    },
    {
      "ref": "piumallick/nuclei-overview-exploratory-analysis",
      "kernel_id": "656313",
      "votes": 2
    },
    {
      "ref": "branislav1991/converting-tnbc-external-data-to-dsb2018-format",
      "kernel_id": "832498",
      "votes": 11
    },
    {
      "ref": "uscyiwei/dnn-image-recognization",
      "kernel_id": "551554",
      "votes": 5
    },
    {
      "ref": "uscyiwei/implement-ai-to-improve-health-industry",
      "kernel_id": "548817",
      "votes": 0
    },
    {
      "ref": "uscyiwei/u-net-cnn",
      "kernel_id": "571739",
      "votes": 4
    },
    {
      "ref": "piotrczapla/tensorflow-u-net-starter-lb-0-34",
      "kernel_id": "702587",
      "votes": 139
    },
    {
      "ref": "jack55/cyclegan-test-set-to-training-set",
      "kernel_id": "846538",
      "votes": 19
    },
    {
      "ref": "carloskl12/traditional-approach",
      "kernel_id": "727456",
      "votes": 4
    },
    {
      "ref": "carloskl12/ajuste-y-umbralizaci-n-dsb-2018",
      "kernel_id": "948881",
      "votes": 1
    },
    {
      "ref": "gremlin97/nucleus-seperation-using-watershed-algorithm",
      "kernel_id": "869862",
      "votes": 22
    },
    {
      "ref": "glenslade/alternative-metrics-kernel",
      "kernel_id": "576099",
      "votes": 33
    },
    {
      "ref": "vishalse/vanilla-clustering-stage2-acc-38",
      "kernel_id": "905135",
      "votes": 8
    },
    {
      "ref": "weiji14/blank-keras-model-template",
      "kernel_id": "569157",
      "votes": 4
    },
    {
      "ref": "weiji14/yet-another-keras-u-net-data-augmentation",
      "kernel_id": "580032",
      "votes": 141
    },
    {
      "ref": "thekevinscott/experimenting-with-image-preparation-techniques",
      "kernel_id": "668658",
      "votes": 3
    },
    {
      "ref": "haydenliu17/keras-u-net-starter-lb-0-277",
      "kernel_id": "593428",
      "votes": 1
    },
    {
      "ref": "dheenorao/keras-u-net-starter-lb-0-277-5dd39e-dheenov",
      "kernel_id": "809436",
      "votes": 3
    },
    {
      "ref": "msl23518/visualize-the-stage1-test-solution",
      "kernel_id": "850228",
      "votes": 10
    },
    {
      "ref": "kjregan120/image-pre-processing",
      "kernel_id": "723057",
      "votes": 4
    },
    {
      "ref": "nirofa95/basic-thresholding-and-morphological-approach",
      "kernel_id": "830897",
      "votes": 9
    },
    {
      "ref": "abdulkarimx2/keras-u-net-starter-lb-0-277",
      "kernel_id": "732781",
      "votes": 2
    },
    {
      "ref": "bmusclow/keras-u-net-starter-lb-0-277",
      "kernel_id": "788831",
      "votes": 0
    },
    {
      "ref": "aroyburman/nuclei-dsb-2018-tensorflow-u-net-score-0-35-9d30fa",
      "kernel_id": "889957",
      "votes": 9
    },
    {
      "ref": "qz9494/keras-u-net-starter-lb-0-277",
      "kernel_id": "777789",
      "votes": 4
    },
    {
      "ref": "adrian1acoran/fork-of-adrian-pineda-moreno",
      "kernel_id": "930805",
      "votes": 1
    },
    {
      "ref": "dingli/keras-u-net-for-nuclei-segmentation",
      "kernel_id": "1196302",
      "votes": 84
    },
    {
      "ref": "rohith007/kernel7fd1d4a99b",
      "kernel_id": "1051555",
      "votes": 0
    },
    {
      "ref": "venkatramani/nuclei-detection-image-analysis",
      "kernel_id": "1547441",
      "votes": 2
    },
    {
      "ref": "nikhilpandey360/keras-u-net-starter-lb-0-277-93ff3b",
      "kernel_id": "1260211",
      "votes": 9
    },
    {
      "ref": "cedrickchee/total-beginner-tutorial-for-cell-imaging",
      "kernel_id": "1631144",
      "votes": 2
    },
    {
      "ref": "ashukr/teaching-notebook-for-total-imaging-newbies-fork",
      "kernel_id": "1316315",
      "votes": 2
    },
    {
      "ref": "digdig/tflearn-u-net-starter",
      "kernel_id": "1542031",
      "votes": 8
    },
    {
      "ref": "gufranmirza/external-h-e-data-with-mask-annotations",
      "kernel_id": "1052795",
      "votes": 0
    },
    {
      "ref": "gufranmirza/data-augmentation-and-tensorflow-u-net",
      "kernel_id": "1052802",
      "votes": 4
    },
    {
      "ref": "ppjanka/background-extraction-by-simple-image-analysis",
      "kernel_id": "1589737",
      "votes": 3
    },
    {
      "ref": "santoshbothe/teaching-notebook-for-total-imaging-newbies",
      "kernel_id": "1732699",
      "votes": 1
    },
    {
      "ref": "minjielu/spot-nuclei-traditional-solution",
      "kernel_id": "1521036",
      "votes": 1
    },
    {
      "ref": "dseth20/datasciencebowl-eda",
      "kernel_id": "1400496",
      "votes": 2
    },
    {
      "ref": "chenmg04/u-net-for-image-segmentation",
      "kernel_id": "2729324",
      "votes": 3
    },
    {
      "ref": "ryu0715/u-net-and-pyramid-net-for-global-feature",
      "kernel_id": "2967738",
      "votes": 4
    },
    {
      "ref": "ankurshukla03/basic-u-net-using-tensorflow",
      "kernel_id": "2000932",
      "votes": 1
    },
    {
      "ref": "jayantaroy/keras-u-net-for-nuclei-assignment",
      "kernel_id": "2263098",
      "votes": 3
    },
    {
      "ref": "twhitehurst3/keras-u-net-semantic-segmentation",
      "kernel_id": "2896793",
      "votes": 14
    },
    {
      "ref": "krishnareddy527/kernel-nuclei",
      "kernel_id": "2908809",
      "votes": 2
    },
    {
      "ref": "yellowduck/keras-u-net-starter-lb-0-277-9696f8",
      "kernel_id": "3154883",
      "votes": 1
    },
    {
      "ref": "valcoder/satmod2mod",
      "kernel_id": "3776534",
      "votes": 1
    },
    {
      "ref": "advaitsave/tensorflow-2-nuclei-segmentation-unet",
      "kernel_id": "3554631",
      "votes": 84
    },
    {
      "ref": "ayubquadri/my-learning-data-science-bowl",
      "kernel_id": "3029713",
      "votes": 2
    },
    {
      "ref": "vbhattbh/unet-cellsegmentation-with-datagenerator",
      "kernel_id": "3738497",
      "votes": 3
    },
    {
      "ref": "hsankesara/u-net-pytorch",
      "kernel_id": "3691606",
      "votes": 61
    },
    {
      "ref": "vulnic/vlad-mrcnn",
      "kernel_id": "3608669",
      "votes": 4
    },
    {
      "ref": "vulnic/vlad-mask-rcnn",
      "kernel_id": "3598811",
      "votes": 1
    },
    {
      "ref": "xxc025/u-net",
      "kernel_id": "3725356",
      "votes": 4
    },
    {
      "ref": "yashmaxnet/u-net-keras",
      "kernel_id": "4641054",
      "votes": 3
    },
    {
      "ref": "sandhaya4u/fork-of-nuclei-overview-to-submission",
      "kernel_id": "4815396",
      "votes": 6
    },
    {
      "ref": "saqib96mobin/image-segmentation-using-unet-in-tensorflow",
      "kernel_id": "4101566",
      "votes": 13
    },
    {
      "ref": "saqib96mobin/segmentation-using-residual-unet-in-tensorflow",
      "kernel_id": "4120351",
      "votes": 15
    },
    {
      "ref": "msripooja/semantic-segmentation-of-nuclei-images",
      "kernel_id": "4033807",
      "votes": 5
    },
    {
      "ref": "sumanthmeenan/ds-bowl",
      "kernel_id": "5613952",
      "votes": 0
    },
    {
      "ref": "sandhaya4u/rle-to-mask-converter",
      "kernel_id": "5660575",
      "votes": 1
    },
    {
      "ref": "gosha207778/keras-u-net",
      "kernel_id": "5059539",
      "votes": 5
    },
    {
      "ref": "sajaldeb25/2018-data-science-bowl-solved-by-resunet",
      "kernel_id": "6144727",
      "votes": 12
    },
    {
      "ref": "hjkim3/teaching-notebook-for-total-imaging-newbies-200207",
      "kernel_id": "7889378",
      "votes": 4
    },
    {
      "ref": "andriihura/imagesegmentation",
      "kernel_id": "7507840",
      "votes": 5
    },
    {
      "ref": "lukasgajdosech/u-net-nuclei",
      "kernel_id": "8720882",
      "votes": 2
    },
    {
      "ref": "arunmohan003/unet-starter-pytorch",
      "kernel_id": "8700640",
      "votes": 87
    },
    {
      "ref": "poskok/u-net-example",
      "kernel_id": "9527918",
      "votes": 2
    },
    {
      "ref": "sainathseelam/teaching-notebook-for-total-imaging-newbies",
      "kernel_id": "9489611",
      "votes": 2
    },
    {
      "ref": "harry418/unet-family-with-keras",
      "kernel_id": "9677665",
      "votes": 9
    },
    {
      "ref": "tjac718/semantic-segmentation-of-nuclei-pytorch",
      "kernel_id": "9561899",
      "votes": 23
    },
    {
      "ref": "eduardopea/kernel486a57104c",
      "kernel_id": "9955819",
      "votes": 0
    },
    {
      "ref": "jaidevd/weighted-loss-unets",
      "kernel_id": "10444919",
      "votes": 3
    },
    {
      "ref": "pieraqua/kernel472c8ddf7d",
      "kernel_id": "10634932",
      "votes": 1
    },
    {
      "ref": "pieraqua/hydra",
      "kernel_id": "10672602",
      "votes": 14
    },
    {
      "ref": "carloscapellan/semantic-segmentation-tf-keras",
      "kernel_id": "10977847",
      "votes": 11
    },
    {
      "ref": "bestofbests9/dsb-2018-dataset-generation",
      "kernel_id": "11315063",
      "votes": 7
    },
    {
      "ref": "firqaaa/residual-unet-keras-implementation",
      "kernel_id": "11382433",
      "votes": 24
    },
    {
      "ref": "rkuo2000/u-net-nucleus",
      "kernel_id": "12055615",
      "votes": 4
    },
    {
      "ref": "godeep48/simple-unet-pytorch",
      "kernel_id": "12640677",
      "votes": 36
    },
    {
      "ref": "akshat0007/nuclei-segmentation-using-u-net",
      "kernel_id": "12284502",
      "votes": 43
    },
    {
      "ref": "omarsalahhemied/segmentation-cancer-cells-task",
      "kernel_id": "12665999",
      "votes": 9
    },
    {
      "ref": "namylase/data-science-bowl-u-net",
      "kernel_id": "12971485",
      "votes": 2
    },
    {
      "ref": "illiapokutsa/semantic-segmentation-model-with-u-net-tf",
      "kernel_id": "12377110",
      "votes": 7
    },
    {
      "ref": "sikandarkhan9370/identification-and-segmentation-of-nuclei-in-cells",
      "kernel_id": "13157918",
      "votes": 3
    },
    {
      "ref": "bryanb/image-segmentation-u-net",
      "kernel_id": "14776612",
      "votes": 393
    },
    {
      "ref": "marwa96/u-net-segmentation",
      "kernel_id": "14188223",
      "votes": 30
    },
    {
      "ref": "sinjoysaha/segmentation-with-u-net",
      "kernel_id": "15963837",
      "votes": 10
    },
    {
      "ref": "smohamedghazali20/segmentation-of-clutterd-cells-pytorch",
      "kernel_id": "15251038",
      "votes": 2
    },
    {
      "ref": "xiongzhna/u-net-for-nuclei-segmentation",
      "kernel_id": "15957814",
      "votes": 1
    },
    {
      "ref": "espsiyam/u-net-image-nuclei-segmentation",
      "kernel_id": "16827247",
      "votes": 23
    },
    {
      "ref": "marinameier/find-the-nuclei-in-divergent-medical-images",
      "kernel_id": "16114693",
      "votes": 3
    },
    {
      "ref": "piotrtrawinski/2018-data-science-bowl",
      "kernel_id": "17746224",
      "votes": 0
    },
    {
      "ref": "gurujaing/ds-bowl-2018",
      "kernel_id": "17689766",
      "votes": 4
    },
    {
      "ref": "rahulsawhney/ghmta-comde",
      "kernel_id": "18543280",
      "votes": 0
    },
    {
      "ref": "dicksonwu/hack3-nuclei-detection",
      "kernel_id": "18115803",
      "votes": 4
    },
    {
      "ref": "karthikpulipati/notebook863a4a0611",
      "kernel_id": "18215496",
      "votes": 1
    },
    {
      "ref": "d5423197/u-net-training",
      "kernel_id": "19629853",
      "votes": 11
    },
    {
      "ref": "mayankrajpurohit/nuclei-segmentation-using-u-net-and-double-u-net",
      "kernel_id": "19264808",
      "votes": 34
    },
    {
      "ref": "tunguz/unique-kaggle-users",
      "kernel_id": "20716426",
      "votes": 69
    },
    {
      "ref": "shakshyathedetector/image-segmentation-using-u-net",
      "kernel_id": "20311084",
      "votes": 26
    },
    {
      "ref": "minhhngchong/calculate-mean-and-std",
      "kernel_id": "21147605",
      "votes": 5
    },
    {
      "ref": "alimohammedbakhiet/learning-image-segmentation",
      "kernel_id": "21277221",
      "votes": 13
    },
    {
      "ref": "anilabhadatta/identification-and-segmentation-of-nuclei-in-cells",
      "kernel_id": "21378422",
      "votes": 11
    },
    {
      "ref": "ramsegaurav/unet-for-image-segmentation",
      "kernel_id": "22436249",
      "votes": 26
    },
    {
      "ref": "abhiswain/unet-pytorchlightning",
      "kernel_id": "23542051",
      "votes": 10
    },
    {
      "ref": "godeep48/unet-with-residual-encoders",
      "kernel_id": "24335953",
      "votes": 25
    },
    {
      "ref": "savindiwijenayaka/datasciencebowl2018",
      "kernel_id": "24087077",
      "votes": 1
    },
    {
      "ref": "maxitype/6-u-net-custom-iou-beginner",
      "kernel_id": "24344658",
      "votes": 57
    },
    {
      "ref": "minhhngchong/medical-image-segmentation-2",
      "kernel_id": "24755255",
      "votes": 20
    },
    {
      "ref": "tejasvdante/medical-image-segmentation-2",
      "kernel_id": "25319699",
      "votes": 2
    },
    {
      "ref": "mohamedmustafa/medical-image-segmentation-using-u-net-with-keras",
      "kernel_id": "25229281",
      "votes": 16
    },
    {
      "ref": "songye123/ese577part2",
      "kernel_id": "26222094",
      "votes": 5
    },
    {
      "ref": "muhammadishaque/unet-works",
      "kernel_id": "28325611",
      "votes": 1
    },
    {
      "ref": "taufiqtusar/image-segmentation-u-net",
      "kernel_id": "28984730",
      "votes": 30
    },
    {
      "ref": "masyitahabu/miniproject-4",
      "kernel_id": "29193267",
      "votes": 0
    },
    {
      "ref": "songty151/unetdemo",
      "kernel_id": "29272197",
      "votes": 1
    },
    {
      "ref": "mbadrismail/unet-datasciencebowl",
      "kernel_id": "29617287",
      "votes": 17
    },
    {
      "ref": "aadyac/u-net-reimplementation-by-aadya-chinubhai",
      "kernel_id": "30958113",
      "votes": 28
    },
    {
      "ref": "invhnd/u-net-practice",
      "kernel_id": "30119816",
      "votes": 11
    },
    {
      "ref": "derrickmwiti/fully-convolutional-neural-network-fcn-model",
      "kernel_id": "32962852",
      "votes": 12
    },
    {
      "ref": "helovict/nuclei-segmentation-unet-ipynb",
      "kernel_id": "32908106",
      "votes": 0
    },
    {
      "ref": "derrickmwiti/u-net-image-segmentation-model",
      "kernel_id": "33393501",
      "votes": 52
    },
    {
      "ref": "konantur/identification-and-segmentation-of-nuclei-in-cells",
      "kernel_id": "33624116",
      "votes": 0
    },
    {
      "ref": "klmsathishkumar/automatic-nucleus-detection",
      "kernel_id": "33986700",
      "votes": 2
    },
    {
      "ref": "ishandandekar/u-net-from-scratch-using-keras-and-tensorflow",
      "kernel_id": "33431587",
      "votes": 80
    },
    {
      "ref": "kuttersn/u-net-task",
      "kernel_id": "33426956",
      "votes": 0
    },
    {
      "ref": "valdesguefa/unettp",
      "kernel_id": "33029079",
      "votes": 0
    },
    {
      "ref": "d2t9k10/notebookfa5d6d597c",
      "kernel_id": "33520663",
      "votes": 0
    },
    {
      "ref": "piyushnagpal/unet-implementation",
      "kernel_id": "34800482",
      "votes": 15
    },
    {
      "ref": "abhipshadash/unet-practice",
      "kernel_id": "34155092",
      "votes": 3
    },
    {
      "ref": "aadyac/project-cv-ml",
      "kernel_id": "35100508",
      "votes": 9
    },
    {
      "ref": "aadyac/comparision-of-activationsfns-img-segmentation",
      "kernel_id": "35943255",
      "votes": 6
    },
    {
      "ref": "razinmkarimi/project-cv-ml",
      "kernel_id": "35966687",
      "votes": 5
    },
    {
      "ref": "aadyac/fork-of-comparision-of-activationsfns-img-s-d09977",
      "kernel_id": "37255229",
      "votes": 7
    },
    {
      "ref": "razinmkarimi/comparision-of-activationsfns-img-segmentation",
      "kernel_id": "37286341",
      "votes": 2
    },
    {
      "ref": "wenweikuang/data-science-bowl-2018",
      "kernel_id": "37775505",
      "votes": 1
    },
    {
      "ref": "pawankumargunjan/2018-data-science-bowl",
      "kernel_id": "38032783",
      "votes": 15
    },
    {
      "ref": "aadyac/leakyrelugaussiannoise",
      "kernel_id": "39012083",
      "votes": 0
    },
    {
      "ref": "rukaiyahasan/image-segmentation-u-net",
      "kernel_id": "39445137",
      "votes": 2
    },
    {
      "ref": "asheniranga/hands-on-image-segmentation-with-pytorch",
      "kernel_id": "42941537",
      "votes": 3
    },
    {
      "ref": "capecrusader27/unet-augmentation-segmentation-models",
      "kernel_id": "43150711",
      "votes": 8
    },
    {
      "ref": "divyareddyrecharla/unet-2d",
      "kernel_id": "43991568",
      "votes": 2
    },
    {
      "ref": "divyareddyrecharla/unet-augmentation-segmentation-models",
      "kernel_id": "44168875",
      "votes": 17
    },
    {
      "ref": "rohitdileep/unet-tf",
      "kernel_id": "47768005",
      "votes": 1
    },
    {
      "ref": "zsombormalatinszki/bioimage-informatics-project",
      "kernel_id": "47795062",
      "votes": 1
    },
    {
      "ref": "nandaprasetia/2018-data-science-bowl-unet-clahe",
      "kernel_id": "47979965",
      "votes": 19
    },
    {
      "ref": "soobysenpai/project-implementation",
      "kernel_id": "47816213",
      "votes": 3
    },
    {
      "ref": "yasha3886/computer-vision-project",
      "kernel_id": "47784299",
      "votes": 1
    },
    {
      "ref": "sameerranjan2206/image-segmentation-u-net",
      "kernel_id": "47035391",
      "votes": 1
    },
    {
      "ref": "jainsuyash2003/cv-project-implementation",
      "kernel_id": "47764271",
      "votes": 0
    },
    {
      "ref": "debasishpalai18/nuclei-segmentation-using-u-net-and-double-u-net",
      "kernel_id": "49077984",
      "votes": 19
    },
    {
      "ref": "aavilacar/microscope-image-segmentation",
      "kernel_id": "49541105",
      "votes": 10
    },
    {
      "ref": "valliansayoga/learning-u-net-using-data-science-bowl-2018-data",
      "kernel_id": "50095744",
      "votes": 12
    },
    {
      "ref": "manellansari/u-net-image-segmentation-model",
      "kernel_id": "51725531",
      "votes": 19
    },
    {
      "ref": "selcukcan/dl-6-3-image-segmentation-using-u-net",
      "kernel_id": "52044329",
      "votes": 11
    },
    {
      "ref": "irbr369/u-net-for-nuclie-segmentation",
      "kernel_id": "52870947",
      "votes": 0
    },
    {
      "ref": "samithanawarathna/tensorflow-mri-segmentation-with-unet-resunet",
      "kernel_id": "52262729",
      "votes": 24
    },
    {
      "ref": "monkira/transunet",
      "kernel_id": "54113241",
      "votes": 8
    },
    {
      "ref": "minjeongv/teaching-notebook-for-total-imaging-newbies",
      "kernel_id": "54675748",
      "votes": 0
    },
    {
      "ref": "b21dccn032vthnht/image-segmentation-unet",
      "kernel_id": "55034916",
      "votes": 8
    },
    {
      "ref": "nanmorsantos/getting-started-image-processing-basics",
      "kernel_id": "57344554",
      "votes": 1
    },
    {
      "ref": "shanksftw/unet-sgd",
      "kernel_id": "57835851",
      "votes": 2
    },
    {
      "ref": "shanksftw/unet-new",
      "kernel_id": "57256742",
      "votes": 1
    },
    {
      "ref": "skandtripathi/unet-nuclei",
      "kernel_id": "60988299",
      "votes": 4
    },
    {
      "ref": "abhiramkamini/image-segmentation-u-net",
      "kernel_id": "61499977",
      "votes": 0
    },
    {
      "ref": "haminhhieu/databowl",
      "kernel_id": "61805593",
      "votes": 0
    },
    {
      "ref": "vijaybj/yolo-pytorch-from-scratch",
      "kernel_id": "63182251",
      "votes": 10
    },
    {
      "ref": "josshhh/unet-cell-segmentation",
      "kernel_id": "64122276",
      "votes": 2
    },
    {
      "ref": "kansalritu/semantic-segmentation",
      "kernel_id": "64301582",
      "votes": 11
    },
    {
      "ref": "aman896/segmentaion-using-u-net",
      "kernel_id": "66362964",
      "votes": 0
    },
    {
      "ref": "safindipto/fca-2018-bowl",
      "kernel_id": "68591419",
      "votes": 14
    },
    {
      "ref": "adeliashafiraardhani/tugas-identification-and-segmentation-of-nuclei",
      "kernel_id": "69496408",
      "votes": 3
    },
    {
      "ref": "beweird/unet-nuclei",
      "kernel_id": "69969883",
      "votes": 0
    },
    {
      "ref": "arumraa/identifikasi-dan-segmentasi-of-nuclei-in-cells",
      "kernel_id": "69589582",
      "votes": 0
    },
    {
      "ref": "lethihongphuong/notebookf8f9076e8c",
      "kernel_id": "73584401",
      "votes": 1
    },
    {
      "ref": "schmitouuuu/cell-segmentation-nicolas-schmitt",
      "kernel_id": "73628205",
      "votes": 0
    },
    {
      "ref": "suddhasattwadas/unet-1-nucli",
      "kernel_id": "74772508",
      "votes": 0
    },
    {
      "ref": "ayushrai781/cellsegunet",
      "kernel_id": "76399275",
      "votes": 2
    },
    {
      "ref": "htrnnhnguynde180372/u-net-bee-varroa",
      "kernel_id": "80402452",
      "votes": 1
    },
    {
      "ref": "ayushrai781/cellsegunet-tune",
      "kernel_id": "83005315",
      "votes": 0
    },
    {
      "ref": "zubairiqbal10/u-net-image-segmentation",
      "kernel_id": "85492453",
      "votes": 5
    },
    {
      "ref": "fardinp/cell-nuclei-segmentation-project",
      "kernel_id": "86091361",
      "votes": 10
    },
    {
      "ref": "radeveljic/image-segmentation-u-net-rade",
      "kernel_id": "90357369",
      "votes": 3
    },
    {
      "ref": "antimamishra004/semantic-segmentation",
      "kernel_id": "90840583",
      "votes": 0
    },
    {
      "ref": "sreeramojuritvik/vicky",
      "kernel_id": "93334815",
      "votes": 0
    },
    {
      "ref": "ttai1302/unet-unetplusplus-segmentation",
      "kernel_id": "97812332",
      "votes": 0
    },
    {
      "ref": "osmansevili/bowl-data-science",
      "kernel_id": "97081981",
      "votes": 1
    },
    {
      "ref": "balirasem/segemenation-nuclei",
      "kernel_id": "98353865",
      "votes": 0
    },
    {
      "ref": "haitemboulahbal/project-aii",
      "kernel_id": "98230850",
      "votes": 2
    },
    {
      "ref": "naveedmuhammadsaad/u-net-and-variants",
      "kernel_id": "98252814",
      "votes": 6
    },
    {
      "ref": "aknietkalzhan/unet-nucleus-akko",
      "kernel_id": "99364772",
      "votes": 6
    },
    {
      "ref": "hoangnguyencanh1010/comva",
      "kernel_id": "100907782",
      "votes": 6
    },
    {
      "ref": "loiwiiz/test-xla",
      "kernel_id": "101841012",
      "votes": 1
    },
    {
      "ref": "min2k4/test-xla",
      "kernel_id": "102835849",
      "votes": 0
    },
    {
      "ref": "gamzebabayigit/bowldatascience",
      "kernel_id": "103098878",
      "votes": 0
    },
    {
      "ref": "mujtaba445/final-superglue-assignment",
      "kernel_id": "110528218",
      "votes": 0
    },
    {
      "ref": "kangsuchang/unet-baseline",
      "kernel_id": "119215594",
      "votes": 2
    },
    {
      "ref": "nguyenhuyhoang1/nestedunet-unet",
      "kernel_id": "125511117",
      "votes": 1
    },
    {
      "ref": "abinj12553/nuclei-segmentation-phenotypic-profiling-for-dru",
      "kernel_id": "126346698",
      "votes": 0
    },
    {
      "ref": "sriramlingavel01/u-net-deeplab",
      "kernel_id": "130622171",
      "votes": 0
    },
    {
      "ref": "parth2157/2018-data-science-blow-studying-cell-ipynb",
      "kernel_id": "135983015",
      "votes": 0
    }
  ],
  "count": 327
}