{"cells":[{"cell_type":"markdown","metadata":{"id":"aR-3PCa0zt7D"},"source":["# Breast Cancer Segmentation Using Transformers\n","\n","- **Semantic segmentation** เป็นการทำนายที่ต้องการนำภาพมาและแบ่งประเภทของแต่ละพิกเซลในภาพ Semantic segmentation มีประโยชน์และถูกนำมาใช้ในหลากหลายแอพพลิเคชั่น เช่น การตัดแบ่งภาพถ่ายทางการแพทย์ และรถยนจ์ขับเคลื่อนอัจฉริยะ ยกตัวอย่างเช่น ในกรณีของรถส่งพิซซ่าอัจโนมัติ การนำ semantic segmentation มาช่วยจะสามารถไกด์ให้รถยังขับอยู่บนทางเท้าได้ ไม่ขับเข้าไปในบริเวณที่ผิดพลาดได้ (ดูตัวอย่างจาก https://huggingface.co/blog/fine-tune-segformer)\n","\n","- วิธีการเก็บข้อมูลของ Semantic segmentation ปกติ จะทำการเก็บภาพที่เราสนใจมาเป็นข้อมูลตั้งต้น จากนั้นใช้ image labeling tools เช่น [Segments.ai](https://segments.ai/) หรือ [CVAT](https://github.com/opencv/cvat) ในการเก็บข้อมูลพิกเซลชนิดต่างๆ (บางครั้งเรียก \"mask\") ในการใช้เป็น label ในการเทรน แต่ในตัวอย่างนี้เราจะใช้ชุดข้อมูลภาพถ่ายอัลตราซาวน์ (ultrasound) ของเต้านม เพื่อตรวจจับเนื้องอกหรือมะเร็งเต้านม\n","\n","- โดยการทำงานของโมเดลจะนำภาพมาทำการทำนายและสรุปว่าแต่ละ pixel ควรเป็นประเภทใด เช่น พื้นหลัง, เนื้องอก, หรือมะเร็ง โดยในกรณีนี้เราจะนำโมเดล [Segformer](https://huggingface.co/docs/transformers/model_doc/segformer) มา fine-tune สำหรับการตรวจจับบริเวณต่างๆที่อาจเป็น เนื้องอก (benign) หรือเนื้อร้าย (malignant) จากภาพถ่ายอัลตราซาวน์ โดยในที่นี้เราจะใช้โมเดลที่ขนาดเล็กที่สุดของ Segformer นั่นคือ `MiT-b0` มาทดลอง fine-tune เพื่อตัดแบ่งภาพของเรา\n","\n","- สำหรับการวัดผลของ Segmentation ส่วนมากจะใช้ Dice Score (เท่ากับ 2 x Overlapped Area / (Actual Area + Predicted Are) ) หรือ Intersection over union (IoU, เท่ากับ Overlapped Area / Union of actual area and predicted area)\n","\n","- ตัวอย่างนี้จะแบ่งเป็นการเตรียมข้อมูลที่โหลดมาได้จาก Kaggle, การนำโมเดล Segformer มา fine-tune, และจากนั้นคือการนำโมเดลไปทำนายผลจากภาพใน validation set\n","\n","\n","Reference: https://huggingface.co/blog/fine-tune-segformer"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27589,"status":"ok","timestamp":1688479857280,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"OLoLjNG4znH5","outputId":"0cd23921-8bb1-4a03-c603-11af6bcf5e52"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.13)\n","Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.5.7)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.27.1)\n","Requirement 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Data"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8504,"status":"ok","timestamp":1688479865779,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"Xo9VZgwbzx8r","outputId":"e6ba057a-0070-4978-9d3e-a58bc99dbf61"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading breast-ultrasound-images-dataset.zip to /content\n"," 92% 180M/195M [00:01<00:00, 171MB/s]\n","100% 195M/195M [00:01<00:00, 184MB/s]\n","Archive: breast-ultrasound-images-dataset.zip\n"," inflating: Dataset_BUSI_with_GT/benign/benign (1).png \n"," inflating: Dataset_BUSI_with_GT/benign/benign (1)_mask.png \n"," inflating: Dataset_BUSI_with_GT/benign/benign (10).png \n"," inflating: Dataset_BUSI_with_GT/benign/benign (10)_mask.png \n"," inflating: Dataset_BUSI_with_GT/benign/benign (100).png \n"," inflating: Dataset_BUSI_with_GT/benign/benign (100)_mask.png \n"," inflating: 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Dataset_BUSI_with_GT/normal/normal (93).png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (93)_mask.png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (94).png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (94)_mask.png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (95).png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (95)_mask.png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (96).png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (96)_mask.png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (97).png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (97)_mask.png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (98).png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (98)_mask.png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (99).png \n"," inflating: Dataset_BUSI_with_GT/normal/normal (99)_mask.png \n"]}],"source":["# อัพโหลด kaggle.json ที่หาได้จาก https://www.kaggle.com/settings (ไปที่หน้านี้แล้วกด \"Create New Token\" ใน section API)\n","!mkdir ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 ~/.kaggle/kaggle.json\n","!kaggle datasets download -d aryashah2k/breast-ultrasound-images-dataset\n","!unzip breast-ultrasound-images-dataset.zip"]},{"cell_type":"code","execution_count":17,"metadata":{"executionInfo":{"elapsed":36,"status":"ok","timestamp":1688479865779,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"CDgAXI4u0Hze"},"outputs":[],"source":["from transformers import SegformerForSemanticSegmentation\n","from transformers import SegformerFeatureExtractor\n","from transformers import TrainingArguments, Trainer\n","from torchvision.transforms import ColorJitter, Resize, Compose\n","\n","import torch\n","from torch import nn\n","import evaluate\n","import datasets\n","from datasets import Dataset, Features\n","import numpy as np\n","\n","import os.path as op\n","from glob import glob\n","from pathlib import Path\n","from PIL import Image\n","from tqdm.auto import tqdm\n","import os"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":35,"status":"ok","timestamp":1688479865779,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"1RSj_XlAz0Ni","outputId":"651eb360-84bc-45f7-ad30-b7595e9caefb"},"outputs":[{"name":"stdout","output_type":"stream","text":["Length of image and segmentation paths = 780\n"]}],"source":["paths = glob(\"Dataset_BUSI_with_GT/*/*\") # get all folders: normal, benign, malignant\n","im_paths = [path for path in paths if \"_mask\" not in path]\n","data_paths = []\n","\n","# สร้าง dictionary ที่รวม image path, masking path, และ class name\n","for path in im_paths:\n"," class_name = Path(path).parent.name\n"," seg_path = Path(Path(path).parent, Path(path).stem + \"_mask.png\")\n"," if op.exists(seg_path):\n"," data_paths.append({\n"," \"img\": path,\n"," \"seg\": str(seg_path),\n"," \"class\": class_name\n"," })\n","print(\"Length of image and segmentation paths = \", len(data_paths))"]},{"cell_type":"code","execution_count":19,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1688479865780,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"qBwhByT7xwkp"},"outputs":[],"source":["# list of dictionary to dictionary of each list\n","data_paths = {\n"," \"img\": [p[\"img\"] for p in data_paths],\n"," \"seg\": [p[\"seg\"] for p in data_paths],\n"," \"class\": [p[\"class\"] for p in data_paths]\n","}\n","\n","# convert ระหว่าง id <-> lable name\n","id2label = {0:\"background\", 1: \"benign\", 2: \"malignant\"}\n","label2id = {v: k for k, v in id2label.items()}"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":557,"status":"ok","timestamp":1688479866330,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"3AGDg9y81bIG"},"outputs":[],"source":["ds = Dataset.from_dict(data_paths, features=Features(\n"," {\"img\": datasets.Image(),\n"," \"seg\": datasets.Image(),\n"," \"class\": datasets.Value(dtype='string', id=None)}))\n","\n","# train test split\n","ds_split = ds.train_test_split(test_size=0.2)"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1688479866330,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"a-rFaY7125KY"},"outputs":[],"source":["train_ds = ds_split[\"train\"]\n","test_ds = ds_split[\"test\"]"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1688479866331,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"JNd8ouJ61-Jq","outputId":"d65bb8bd-c522-455b-adf8-1088910f5217"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/transformers/models/segformer/feature_extraction_segformer.py:28: FutureWarning: The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use SegformerImageProcessor instead.\n"," warnings.warn(\n"]}],"source":["feature_extractor = SegformerFeatureExtractor()\n","\n","# กำหนด transformsที่ต้องการ\n","_transforms = Compose([\n"," ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1),\n","])"]},{"cell_type":"code","execution_count":23,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1688479866331,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"abtxGvEzpKic"},"outputs":[],"source":["def train_transforms(example_batch):\n"," '''\n"," pipeline ในการ transfrom train data\n"," ถ้าเป็น mask ของ class benign จะให้value = 1\n"," และถ้าเป็น class malignant จะเป็น value = 2\n"," และลด channel ให้เหลือ 1 channel\n"," '''\n"," images = [_transforms(x) for x in example_batch[\"img\"]]\n"," labels = []\n"," for segim, cls in zip(example_batch[\"seg\"], example_batch[\"class\"]):\n"," segim = np.array(segim).astype(int)\n"," if cls == \"benign\":\n"," segim[segim == 1] = 1\n"," elif cls == \"malignant\":\n"," segim[segim == 1] = 2\n"," else:\n"," pass\n"," if len(segim.shape) == 3:\n"," segim = segim[:, :, 0]\n"," labels.append(segim)\n"," inputs = feature_extractor(images, labels)\n"," return inputs\n","\n","def val_transforms(example_batch):\n"," images = [x for x in example_batch[\"img\"]]\n"," labels = []\n"," for segim, cls in zip(example_batch[\"seg\"], example_batch[\"class\"]):\n"," segim = np.array(segim).astype(int)\n"," if cls == \"benign\":\n"," segim[segim == 1] = 1\n"," elif cls == \"malignant\":\n"," segim[segim == 1] = 2\n"," else:\n"," pass\n"," if len(segim.shape) == 3:\n"," segim = segim[:, :, 0]\n"," labels.append(segim)\n"," inputs = feature_extractor(images, labels)\n"," return inputs"]},{"cell_type":"markdown","metadata":{"id":"Bq84-2-gBiYi"},"source":["input function สำหรับ transfrom data"]},{"cell_type":"code","execution_count":24,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1688479866331,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"x_c_EeDJ_6QT"},"outputs":[],"source":["train_ds.set_transform(train_transforms)\n","test_ds.set_transform(val_transforms)"]},{"cell_type":"markdown","metadata":{"id":"5iPE7u7nD84L"},"source":["function ที่ใช้ในการ evaluate"]},{"cell_type":"code","execution_count":25,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1688479866332,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"YshZurDdpKid"},"outputs":[],"source":["num_labels = len(id2label)\n","metric = evaluate.load(\"mean_iou\")\n","\n","def compute_metrics(eval_pred):\n"," '''\n"," process สำหรับการ evaluate ผลลัพธ์ ที่ทำนายจากโมเดล\n"," '''\n"," # ไม่ต้องคิด gradient\n"," with torch.no_grad():\n"," logits, labels = eval_pred\n"," logits_tensor = torch.from_numpy(logits)\n"," logits_tensor = nn.functional.interpolate(\n"," logits_tensor,\n"," size=labels.shape[-2:],\n"," mode=\"bilinear\",\n"," align_corners=False,\n"," ).argmax(dim=1)\n","\n"," pred_labels = logits_tensor.detach().cpu().numpy()\n","\n"," # คำนวน evaluation metric\n"," metrics = metric.compute(\n"," predictions=pred_labels,\n"," references=labels,\n"," num_labels=num_labels,\n"," ignore_index=255,\n"," reduce_labels=False,\n"," )\n"," for key, value in metrics.items():\n"," if type(value) is np.ndarray:\n"," metrics[key] = value.tolist()\n"," return metrics"]},{"cell_type":"markdown","metadata":{},"source":["## Download a Pretrained Model and Train It"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":654,"status":"ok","timestamp":1688479866982,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"OQambUM3WXVA","outputId":"e1dad995-c1cd-45b3-b18e-5802bec1933a"},"outputs":[{"name":"stderr","output_type":"stream","text":["Some weights of the model checkpoint at nvidia/mit-b0 were not used when initializing SegformerForSemanticSegmentation: ['classifier.weight', 'classifier.bias']\n","- This IS expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing SegformerForSemanticSegmentation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/mit-b0 and are newly initialized: ['decode_head.linear_fuse.weight', 'decode_head.linear_c.3.proj.bias', 'decode_head.batch_norm.weight', 'decode_head.batch_norm.num_batches_tracked', 'decode_head.batch_norm.bias', 'decode_head.linear_c.2.proj.bias', 'decode_head.linear_c.2.proj.weight', 'decode_head.classifier.weight', 'decode_head.batch_norm.running_mean', 'decode_head.linear_c.0.proj.weight', 'decode_head.linear_c.1.proj.weight', 'decode_head.linear_c.1.proj.bias', 'decode_head.linear_c.0.proj.bias', 'decode_head.linear_c.3.proj.weight', 'decode_head.batch_norm.running_var', 'decode_head.classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}],"source":["# สร้างโมเดลจาก pretrain model\n","pretrained_model_name = \"nvidia/mit-b0\"\n","model = SegformerForSemanticSegmentation.from_pretrained(\n"," pretrained_model_name,\n"," id2label=id2label,\n"," label2id=label2id\n",")"]},{"cell_type":"code","execution_count":27,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1688479866982,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"hCBPmV5RpKid"},"outputs":[],"source":["save_path = \"segformer-breast-cancer_30ep\""]},{"cell_type":"markdown","metadata":{"id":"tcA8QXMIEeWN"},"source":["เทรน 30 epochs"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":766},"executionInfo":{"elapsed":3965630,"status":"ok","timestamp":1688483832607,"user":{"displayName":"korrawiz Chotayapa","userId":"13359737025054536148"},"user_tz":-420},"id":"_zk3BtDIWg-U","outputId":"13e6090e-9b1c-4695-faf3-f5f0a765622d"},"outputs":[{"name":"stderr","output_type":"stream","text":["Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n","/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n"," warnings.warn(\n"]},{"data":{"text/html":["\n","
Step | \n","Training Loss | \n","Validation Loss | \n","Mean Iou | \n","Mean Accuracy | \n","Overall Accuracy | \n","Per Category Iou | \n","Per Category Accuracy | \n","
---|---|---|---|---|---|---|---|
300 | \n","0.180500 | \n","0.207105 | \n","0.563584 | \n","0.650218 | \n","0.945108 | \n","[0.9522910214628129, 0.35597900507094016, 0.38248337122310894] | \n","[0.9844119296714371, 0.4803877884345996, 0.48585527883746626] | \n","
600 | \n","0.152000 | \n","0.162783 | \n","0.604853 | \n","0.713426 | \n","0.947683 | \n","[0.9525706140330837, 0.4258345563794154, 0.43615323108476856] | \n","[0.9791855052811275, 0.5975566139521575, 0.5635362311699621] | \n","
900 | \n","0.043500 | \n","0.151741 | \n","0.628168 | \n","0.708019 | \n","0.954531 | \n","[0.9580944032975929, 0.4604088037923396, 0.46600137880435566] | \n","[0.9878961064559273, 0.6007323005025066, 0.5354272765687837] | \n","
1200 | \n","0.078100 | \n","0.146854 | \n","0.648438 | \n","0.743815 | \n","0.955529 | \n","[0.9586950345597687, 0.46937472529471586, 0.5172430492953498] | \n","[0.983367143662574, 0.5959896339391627, 0.652088757114403] | \n","
1500 | \n","0.035200 | \n","0.153306 | \n","0.647511 | \n","0.747619 | \n","0.954053 | \n","[0.9565895254437337, 0.47311257865553547, 0.5128303326014411] | \n","[0.9806451753285087, 0.5658362591574818, 0.6963768026680048] | \n","
1800 | \n","0.100400 | \n","0.157909 | \n","0.658462 | \n","0.733171 | \n","0.958309 | \n","[0.9603114527087526, 0.4867013048043347, 0.5283727513095081] | \n","[0.9881126634523039, 0.5895808868674706, 0.6218209122610078] | \n","
2100 | \n","0.020100 | \n","0.161642 | \n","0.654843 | \n","0.734376 | \n","0.957776 | \n","[0.9603979053980308, 0.47884308927633784, 0.5252888038447846] | \n","[0.9872288158312638, 0.5836944664667767, 0.632204579889383] | \n","
"],"text/plain":["