Breast Cancer Segmentation Using Transformers#
Semantic segmentation เป็นการทำนายที่ต้องการนำภาพมาและแบ่งประเภทของแต่ละพิกเซลในภาพ Semantic segmentation มีประโยชน์และถูกนำมาใช้ในหลากหลายแอพพลิเคชั่น เช่น การตัดแบ่งภาพถ่ายทางการแพทย์ และรถยนจ์ขับเคลื่อนอัจฉริยะ ยกตัวอย่างเช่น ในกรณีของรถส่งพิซซ่าอัจโนมัติ การนำ semantic segmentation มาช่วยจะสามารถไกด์ให้รถยังขับอยู่บนทางเท้าได้ ไม่ขับเข้าไปในบริเวณที่ผิดพลาดได้ (ดูตัวอย่างจาก https://huggingface.co/blog/fine-tune-segformer)
วิธีการเก็บข้อมูลของ Semantic segmentation ปกติ จะทำการเก็บภาพที่เราสนใจมาเป็นข้อมูลตั้งต้น จากนั้นใช้ image labeling tools เช่น Segments.ai หรือ CVAT ในการเก็บข้อมูลพิกเซลชนิดต่างๆ (บางครั้งเรียก “mask”) ในการใช้เป็น label ในการเทรน แต่ในตัวอย่างนี้เราจะใช้ชุดข้อมูลภาพถ่ายอัลตราซาวน์ (ultrasound) ของเต้านม เพื่อตรวจจับเนื้องอกหรือมะเร็งเต้านม
โดยการทำงานของโมเดลจะนำภาพมาทำการทำนายและสรุปว่าแต่ละ pixel ควรเป็นประเภทใด เช่น พื้นหลัง, เนื้องอก, หรือมะเร็ง โดยในกรณีนี้เราจะนำโมเดล Segformer มา fine-tune สำหรับการตรวจจับบริเวณต่างๆที่อาจเป็น เนื้องอก (benign) หรือเนื้อร้าย (malignant) จากภาพถ่ายอัลตราซาวน์ โดยในที่นี้เราจะใช้โมเดลที่ขนาดเล็กที่สุดของ Segformer นั่นคือ
MiT-b0มาทดลอง fine-tune เพื่อตัดแบ่งภาพของเราสำหรับการวัดผลของ 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)
ตัวอย่างนี้จะแบ่งเป็นการเตรียมข้อมูลที่โหลดมาได้จาก Kaggle, การนำโมเดล Segformer มา fine-tune, และจากนั้นคือการนำโมเดลไปทำนายผลจากภาพใน validation set
Reference: https://huggingface.co/blog/fine-tune-segformer
!pip install kaggle
!pip install transformers
!pip install datasets
!pip install evaluate
!pip install transformers[torch]
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Download Data#
# อัพโหลด kaggle.json ที่หาได้จาก https://www.kaggle.com/settings (ไปที่หน้านี้แล้วกด "Create New Token" ใน section API)
!mkdir ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
!kaggle datasets download -d aryashah2k/breast-ultrasound-images-dataset
!unzip breast-ultrasound-images-dataset.zip
Downloading breast-ultrasound-images-dataset.zip to /content
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Archive: breast-ultrasound-images-dataset.zip
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from transformers import SegformerForSemanticSegmentation
from transformers import SegformerFeatureExtractor
from transformers import TrainingArguments, Trainer
from torchvision.transforms import ColorJitter, Resize, Compose
import torch
from torch import nn
import evaluate
import datasets
from datasets import Dataset, Features
import numpy as np
import os.path as op
from glob import glob
from pathlib import Path
from PIL import Image
from tqdm.auto import tqdm
import os
paths = glob("Dataset_BUSI_with_GT/*/*") # get all folders: normal, benign, malignant
im_paths = [path for path in paths if "_mask" not in path]
data_paths = []
# สร้าง dictionary ที่รวม image path, masking path, และ class name
for path in im_paths:
class_name = Path(path).parent.name
seg_path = Path(Path(path).parent, Path(path).stem + "_mask.png")
if op.exists(seg_path):
data_paths.append({
"img": path,
"seg": str(seg_path),
"class": class_name
})
print("Length of image and segmentation paths = ", len(data_paths))
Length of image and segmentation paths = 780
# list of dictionary to dictionary of each list
data_paths = {
"img": [p["img"] for p in data_paths],
"seg": [p["seg"] for p in data_paths],
"class": [p["class"] for p in data_paths]
}
# convert ระหว่าง id <-> lable name
id2label = {0:"background", 1: "benign", 2: "malignant"}
label2id = {v: k for k, v in id2label.items()}
ds = Dataset.from_dict(data_paths, features=Features(
{"img": datasets.Image(),
"seg": datasets.Image(),
"class": datasets.Value(dtype='string', id=None)}))
# train test split
ds_split = ds.train_test_split(test_size=0.2)
train_ds = ds_split["train"]
test_ds = ds_split["test"]
feature_extractor = SegformerFeatureExtractor()
# กำหนด transformsที่ต้องการ
_transforms = Compose([
ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1),
])
/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.
warnings.warn(
def train_transforms(example_batch):
'''
pipeline ในการ transfrom train data
ถ้าเป็น mask ของ class benign จะให้value = 1
และถ้าเป็น class malignant จะเป็น value = 2
และลด channel ให้เหลือ 1 channel
'''
images = [_transforms(x) for x in example_batch["img"]]
labels = []
for segim, cls in zip(example_batch["seg"], example_batch["class"]):
segim = np.array(segim).astype(int)
if cls == "benign":
segim[segim == 1] = 1
elif cls == "malignant":
segim[segim == 1] = 2
else:
pass
if len(segim.shape) == 3:
segim = segim[:, :, 0]
labels.append(segim)
inputs = feature_extractor(images, labels)
return inputs
def val_transforms(example_batch):
images = [x for x in example_batch["img"]]
labels = []
for segim, cls in zip(example_batch["seg"], example_batch["class"]):
segim = np.array(segim).astype(int)
if cls == "benign":
segim[segim == 1] = 1
elif cls == "malignant":
segim[segim == 1] = 2
else:
pass
if len(segim.shape) == 3:
segim = segim[:, :, 0]
labels.append(segim)
inputs = feature_extractor(images, labels)
return inputs
input function สำหรับ transfrom data
train_ds.set_transform(train_transforms)
test_ds.set_transform(val_transforms)
function ที่ใช้ในการ evaluate
num_labels = len(id2label)
metric = evaluate.load("mean_iou")
def compute_metrics(eval_pred):
'''
process สำหรับการ evaluate ผลลัพธ์ ที่ทำนายจากโมเดล
'''
# ไม่ต้องคิด gradient
with torch.no_grad():
logits, labels = eval_pred
logits_tensor = torch.from_numpy(logits)
logits_tensor = nn.functional.interpolate(
logits_tensor,
size=labels.shape[-2:],
mode="bilinear",
align_corners=False,
).argmax(dim=1)
pred_labels = logits_tensor.detach().cpu().numpy()
# คำนวน evaluation metric
metrics = metric.compute(
predictions=pred_labels,
references=labels,
num_labels=num_labels,
ignore_index=255,
reduce_labels=False,
)
for key, value in metrics.items():
if type(value) is np.ndarray:
metrics[key] = value.tolist()
return metrics
Download a Pretrained Model and Train It#
# สร้างโมเดลจาก pretrain model
pretrained_model_name = "nvidia/mit-b0"
model = SegformerForSemanticSegmentation.from_pretrained(
pretrained_model_name,
id2label=id2label,
label2id=label2id
)
Some weights of the model checkpoint at nvidia/mit-b0 were not used when initializing SegformerForSemanticSegmentation: ['classifier.weight', 'classifier.bias']
- 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).
- 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).
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']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
save_path = "segformer-breast-cancer_30ep"
เทรน 30 epochs
os.environ["WANDB_DISABLED"] = "true"
n_epochs = 30
training_args = TrainingArguments(
output_dir= save_path,
learning_rate=6e-5,
num_train_epochs=n_epochs,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
save_total_limit=3,
evaluation_strategy="steps",
save_strategy="steps",
save_steps=300,
eval_steps=300,
logging_steps=1,
eval_accumulation_steps=5,
remove_unused_columns=False,
push_to_hub=False,
load_best_model_at_end=True,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset=test_ds,
compute_metrics=compute_metrics,
)
trainer.train()
feature_extractor.save_pretrained(save_path)
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).
/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
warnings.warn(
[2340/2340 1:05:53, Epoch 30/30]
| Step | Training Loss | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|---|---|---|---|---|---|---|---|
| 300 | 0.180500 | 0.207105 | 0.563584 | 0.650218 | 0.945108 | [0.9522910214628129, 0.35597900507094016, 0.38248337122310894] | [0.9844119296714371, 0.4803877884345996, 0.48585527883746626] |
| 600 | 0.152000 | 0.162783 | 0.604853 | 0.713426 | 0.947683 | [0.9525706140330837, 0.4258345563794154, 0.43615323108476856] | [0.9791855052811275, 0.5975566139521575, 0.5635362311699621] |
| 900 | 0.043500 | 0.151741 | 0.628168 | 0.708019 | 0.954531 | [0.9580944032975929, 0.4604088037923396, 0.46600137880435566] | [0.9878961064559273, 0.6007323005025066, 0.5354272765687837] |
| 1200 | 0.078100 | 0.146854 | 0.648438 | 0.743815 | 0.955529 | [0.9586950345597687, 0.46937472529471586, 0.5172430492953498] | [0.983367143662574, 0.5959896339391627, 0.652088757114403] |
| 1500 | 0.035200 | 0.153306 | 0.647511 | 0.747619 | 0.954053 | [0.9565895254437337, 0.47311257865553547, 0.5128303326014411] | [0.9806451753285087, 0.5658362591574818, 0.6963768026680048] |
| 1800 | 0.100400 | 0.157909 | 0.658462 | 0.733171 | 0.958309 | [0.9603114527087526, 0.4867013048043347, 0.5283727513095081] | [0.9881126634523039, 0.5895808868674706, 0.6218209122610078] |
| 2100 | 0.020100 | 0.161642 | 0.654843 | 0.734376 | 0.957776 | [0.9603979053980308, 0.47884308927633784, 0.5252888038447846] | [0.9872288158312638, 0.5836944664667767, 0.632204579889383] |
Trainer is attempting to log a value of "[0.9522910214628129, 0.35597900507094016, 0.38248337122310894]" of type <class 'list'> for key "eval/per_category_iou" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9844119296714371, 0.4803877884345996, 0.48585527883746626]" of type <class 'list'> for key "eval/per_category_accuracy" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9525706140330837, 0.4258345563794154, 0.43615323108476856]" of type <class 'list'> for key "eval/per_category_iou" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9791855052811275, 0.5975566139521575, 0.5635362311699621]" of type <class 'list'> for key "eval/per_category_accuracy" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9580944032975929, 0.4604088037923396, 0.46600137880435566]" of type <class 'list'> for key "eval/per_category_iou" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9878961064559273, 0.6007323005025066, 0.5354272765687837]" of type <class 'list'> for key "eval/per_category_accuracy" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9586950345597687, 0.46937472529471586, 0.5172430492953498]" of type <class 'list'> for key "eval/per_category_iou" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.983367143662574, 0.5959896339391627, 0.652088757114403]" of type <class 'list'> for key "eval/per_category_accuracy" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9565895254437337, 0.47311257865553547, 0.5128303326014411]" of type <class 'list'> for key "eval/per_category_iou" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9806451753285087, 0.5658362591574818, 0.6963768026680048]" of type <class 'list'> for key "eval/per_category_accuracy" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9603114527087526, 0.4867013048043347, 0.5283727513095081]" of type <class 'list'> for key "eval/per_category_iou" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9881126634523039, 0.5895808868674706, 0.6218209122610078]" of type <class 'list'> for key "eval/per_category_accuracy" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9603979053980308, 0.47884308927633784, 0.5252888038447846]" of type <class 'list'> for key "eval/per_category_iou" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
Trainer is attempting to log a value of "[0.9872288158312638, 0.5836944664667767, 0.632204579889383]" of type <class 'list'> for key "eval/per_category_accuracy" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.
['segformer-breast-cancer_30ep/preprocessor_config.json']
Inference#
ทดลองใช้จริง โดยการเตรียมข้อมูลจะคล้ายกับการเตรียมข้อมูลสำหรับเทรนโมเดล
# [optional] copy preprocessor_config.json to the model path folder
!cp segformer-breast-cancer_30ep/preprocessor_config.json segformer-breast-cancer_30ep/checkpoint-2100/preprocessor_config.json
print(save_path)
model_path = f"{save_path}/checkpoint-2100"
image_processor = feature_extractor.from_pretrained(f"{save_path}/preprocessor_config.json", local_files_only=True)
model = SegformerForSemanticSegmentation.from_pretrained(f"{model_path}",id2label=id2label, label2id=label2id, local_files_only=True)
segformer-breast-cancer_30ep
/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.
warnings.warn(
ds = Dataset.from_dict(data_paths, features=Features(
{"img": datasets.Image(),
"seg": datasets.Image(),
"class": datasets.Value(dtype='string', id=None)}))
ds_split = ds.train_test_split(test_size=0.2)
train_ds = ds_split["train"]
test_ds = ds_split["test"]
from torch import nn
import matplotlib.pyplot as plt
import numpy as np
image = test_ds[22]['img']
gt_seg = test_ds[22]['seg']
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4)
upsampled_logits = nn.functional.interpolate(
logits,
size=(image.size[1], image.size[0]),
mode="bilinear",
align_corners=False,
)
pred_seg = upsampled_logits.argmax(dim=1)[0]
plt.figure()
plt.subplot(1, 3, 1)
plt.imshow(image)
plt.title("Ultrasound Image")
plt.axis("off")
plt.subplot(1, 3, 2)
plt.imshow(pred_seg, cmap="gray")
plt.title("Prediction")
plt.axis("off")
plt.subplot(1, 3, 3)
plt.imshow(gt_seg, cmap="gray")
plt.title("Ground Truth")
plt.axis("off")
(-0.5, 605.5, 604.5, -0.5)
image = Image.open("./Dataset_BUSI_with_GT/benign/benign (1).png")
gt = Image.open("./Dataset_BUSI_with_GT/benign/benign (1)_mask.png")
from transformers import pipeline
segmenter = pipeline("image-segmentation", model=model_path)
segmenter(image)
/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.
warnings.warn(
[{'score': None,
'label': 'background',
'mask': <PIL.Image.Image image mode=L size=562x471 at 0x7F821418A500>},
{'score': None,
'label': 'benign',
'mask': <PIL.Image.Image image mode=L size=562x471 at 0x7F8210DC6C20>}]
encoding = feature_extractor(image, return_tensors="pt")
pixel_values = encoding.pixel_values
outputs = model(pixel_values=pixel_values)
logits = outputs.logits.cpu()
upsampled_logits = nn.functional.interpolate(
logits,
size=image.size[::-1],
mode="bilinear",
align_corners=False,
)
pred_seg = upsampled_logits.argmax(dim=1)[0]
def ade_palette():
"""Creates a label colormap used in ADE20K segmentation benchmark.
Returns:
A colormap for visualizing segmentation results.
"""
return np.asarray([
[0, 0, 0],
[120, 120, 80],
[140, 140, 140],
[204, 5, 255],
[230, 230, 230],
])
import matplotlib.pyplot as plt
import numpy as np
color_seg = np.zeros((pred_seg.shape[0], pred_seg.shape[1], 3), dtype=np.uint8)
palette = np.array(ade_palette())
for label, color in enumerate(palette):
color_seg[pred_seg == label, :] = color
color_seg = color_seg[..., ::-1] # convert to BGR
img = np.array(image) * 0.5 + color_seg * 0.5 # plot the image with the segmentation map
img = img.astype(np.uint8)
plt.figure(figsize=(15, 10))
plt.imshow(img)
plt.axis('off')
plt.show()
ผู้จัดเตรียม code ใน tutorial: ดร. ฐิติพัทธ อัชชะกุลวิสุทธิ์ และ Peeranut Buabang