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View Code? Open in Web Editor NEWUsing DUCK-Net for polyp image segmentation. ( Nature Scientific Reports 2023 )
License: Creative Commons Attribution 4.0 International
Using DUCK-Net for polyp image segmentation. ( Nature Scientific Reports 2023 )
License: Creative Commons Attribution 4.0 International
Dear Sir, Thank you for your code and results. After following all your instruction I could run the code, but I haven't understood with the qualitative results, in which folder it is saved? How do we get it.
module 'cv2' has no attribute 'CV_8U'
Widescope simulates a kernel size of 15 × 15,you use dilation_rate=1,2,3,How does it simulate 15 * 15 ? Isn't it 13 * 13
Thanks for your interesting research. Do you have any plan to implement this research in Pytorch?
In the model training in the 16th line
model.fit(x=image_augmented, y=mask_augmented, epochs=1, batch_size=4, validation_data=(x_valid, y_valid), verbose=1, callbacks=[csv_logger])
NotFoundError: ProgressFull/kvasir_progress_csv_DuckNet_filters_17_2023-12-01 20:02:22.394228.csv; No such file or directory.
Error is coming.
I am using KVASIR dataset for training.
Please help
can you please post your trained weights?
I think there is a small error in the "requirements.txt" file containing the necessary libraries. The module tensorflow = 2.9.1 depends on keras<2.10, but keras=2.10 is specified in the file. Sorry if I overlooked something, I am quite new to this.
Hi,
I hope this message finds you well. I am currently facing an issue with accessing the test dataset provided. It seems that the test dataset is no longer accessible. Could you please provide it again?
Thank you very much for your assistance.
When I run into X, Y = ImageLoader2D.load_data(img_size, img_size, -1, 'kvasir'), I encountered the following error:
Resizing training images and masks: 1000
0it [00:00, ?it/s]
Traceback (most recent call last):
File "/home/gufei/rfi_code/DUCK-Net/train.py", line 41, in
X, Y = ImageLoader2D.load_data(img_size, img_size, -1, 'kvasir')
File "/home/gufei/rfi_code/DUCK-Net/ImageLoader/ImageLoader2D.py", line 64, in load_data
if mask_[i, j] >= 127:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
I was not able to reproduce the results on this particular dataset that were provided in the research paper.
Everything was kept on default settings with parameters double checked but still could not achieve the desired results
Dice Score
Proposed: 0.9324
Achieved: 0.8485
# Importing the necessary libraries
import tensorflow as tf
import albumentations as albu
import numpy as np
import gc
import pickle
import matplotlib.pyplot as plt
from keras.callbacks import CSVLogger
from datetime import datetime
from sklearn.model_selection import train_test_split
from sklearn.metrics import jaccard_score, precision_score, recall_score, accuracy_score, f1_score
from ModelArchitecture.DiceLoss import dice_metric_loss
from ModelArchitecture import DUCK_Net
from ImageLoader import ImageLoader2D
# Checking the number of GPUs available
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
# Setting the model parameters
img_size = 352
dataset_type = 'etis-laribpolypdb' # Options: kvasir/cvc-clinicdb/cvc-colondb/etis-laribpolypdb
learning_rate = 1e-4
seed_value = 58800
filters = 17 # Number of filters, the paper presents the results with 17 and 34
optimizer = tf.keras.optimizers.RMSprop(learning_rate=learning_rate)
ct = datetime.now()
model_type = "DuckNet"
progress_path = 'ProgressFull/' + dataset_type + '_progress_csv_' + model_type + '_filters_' + str(filters) + '_' + str(ct) + '.csv'
progressfull_path = 'ProgressFull/' + dataset_type + '_progress_' + model_type + '_filters_' + str(filters) + '_' + str(ct) + '.txt'
plot_path = 'ProgressFull/' + dataset_type + '_progress_plot_' + model_type + '_filters_' + str(filters) + '_' + str(ct) + '.png'
model_path = 'ModelSaveTensorFlow/' + dataset_type + '/' + model_type + '_filters_' + str(filters) + '_' + str(ct)
EPOCHS = 600
min_loss_for_saving = 0.2
# Loading the data
X, Y = ImageLoader2D.load_data(img_size, img_size, -1, 'etis-laribpolypdb')
# Splitting the data, seed for reproducibility
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, shuffle= True, random_state = seed_value)
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.111, shuffle= True, random_state = seed_value)
# Defining the augmentations
aug_train = albu.Compose([
albu.HorizontalFlip(),
albu.VerticalFlip(),
albu.ColorJitter(brightness=(0.6,1.6), contrast=0.2, saturation=0.1, hue=0.01, always_apply=True),
albu.Affine(scale=(0.5,1.5), translate_percent=(-0.125,0.125), rotate=(-180,180), shear=(-22.5,22), always_apply=True),
])
def augment_images():
x_train_out = []
y_train_out = []
for i in range (len(x_train)):
ug = aug_train(image=x_train[i], mask=y_train[i])
x_train_out.append(ug['image'])
y_train_out.append(ug['mask'])
return np.array(x_train_out), np.array(y_train_out)
# Creating the model
gpus = tf.config.list_physical_devices('GPU')
if gpus:
# Restrict TensorFlow to only allocate 40GB of memory on the GPU
try:
tf.config.set_logical_device_configuration(
gpus[0],
[tf.config.LogicalDeviceConfiguration(memory_limit=40000)])
logical_gpus = tf.config.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)
model = DUCK_Net.create_model(img_height=img_size, img_width=img_size, input_chanels=3, out_classes=1, starting_filters=filters)
# Compiling the model
model.compile(optimizer=optimizer, loss=dice_metric_loss)
# Training the model
step = 0
for epoch in range(0, EPOCHS):
print(f'Training, epoch {epoch}')
print('Learning Rate: ' + str(learning_rate))
step += 1
image_augmented, mask_augmented = augment_images()
csv_logger = CSVLogger(progress_path, append=True, separator=';')
model.fit(x=image_augmented, y=mask_augmented, epochs=1, batch_size=4, validation_data=(x_valid, y_valid), verbose=1, callbacks=[csv_logger])
prediction_valid = model.predict(x_valid, verbose=0)
loss_valid = dice_metric_loss(y_valid, prediction_valid)
loss_valid = loss_valid.numpy()
print("Loss Validation: " + str(loss_valid))
prediction_test = model.predict(x_test, verbose=0)
loss_test = dice_metric_loss(y_test, prediction_test)
loss_test = loss_test.numpy()
print("Loss Test: " + str(loss_test))
with open(progressfull_path, 'a') as f:
f.write('epoch: ' + str(epoch) + '\nval_loss: ' + str(loss_valid) + '\ntest_loss: ' + str(loss_test) + '\n\n\n')
if min_loss_for_saving > loss_valid:
min_loss_for_saving = loss_valid
print("Saved model with val_loss: ", loss_valid)
model.save(model_path)
del image_augmented
del mask_augmented
gc.collect()
# Computing the metrics and saving the results
print("Loading the model")
model = tf.keras.models.load_model(model_path, custom_objects={'dice_metric_loss':dice_metric_loss})
prediction_train = model.predict(x_train, batch_size=4)
prediction_valid = model.predict(x_valid, batch_size=4)
prediction_test = model.predict(x_test, batch_size=4)
print("Predictions done")
dice_train = f1_score(np.ndarray.flatten(np.array(y_train, dtype=bool)),
np.ndarray.flatten(prediction_train > 0.5))
dice_test = f1_score(np.ndarray.flatten(np.array(y_test, dtype=bool)),
np.ndarray.flatten(prediction_test > 0.5))
dice_valid = f1_score(np.ndarray.flatten(np.array(y_valid, dtype=bool)),
np.ndarray.flatten(prediction_valid > 0.5))
print("Dice finished")
miou_train = jaccard_score(np.ndarray.flatten(np.array(y_train, dtype=bool)),
np.ndarray.flatten(prediction_train > 0.5))
miou_test = jaccard_score(np.ndarray.flatten(np.array(y_test, dtype=bool)),
np.ndarray.flatten(prediction_test > 0.5))
miou_valid = jaccard_score(np.ndarray.flatten(np.array(y_valid, dtype=bool)),
np.ndarray.flatten(prediction_valid > 0.5))
print("Miou finished")
precision_train = precision_score(np.ndarray.flatten(np.array(y_train, dtype=bool)),
np.ndarray.flatten(prediction_train > 0.5))
precision_test = precision_score(np.ndarray.flatten(np.array(y_test, dtype=bool)),
np.ndarray.flatten(prediction_test > 0.5))
precision_valid = precision_score(np.ndarray.flatten(np.array(y_valid, dtype=bool)),
np.ndarray.flatten(prediction_valid > 0.5))
print("Precision finished")
recall_train = recall_score(np.ndarray.flatten(np.array(y_train, dtype=bool)),
np.ndarray.flatten(prediction_train > 0.5))
recall_test = recall_score(np.ndarray.flatten(np.array(y_test, dtype=bool)),
np.ndarray.flatten(prediction_test > 0.5))
recall_valid = recall_score(np.ndarray.flatten(np.array(y_valid, dtype=bool)),
np.ndarray.flatten(prediction_valid > 0.5))
print("Recall finished")
accuracy_train = accuracy_score(np.ndarray.flatten(np.array(y_train, dtype=bool)),
np.ndarray.flatten(prediction_train > 0.5))
accuracy_test = accuracy_score(np.ndarray.flatten(np.array(y_test, dtype=bool)),
np.ndarray.flatten(prediction_test > 0.5))
accuracy_valid = accuracy_score(np.ndarray.flatten(np.array(y_valid, dtype=bool)),
np.ndarray.flatten(prediction_valid > 0.5))
print("Accuracy finished")
final_file = 'results_' + model_type + '_' + str(filters) + '_' + dataset_type + '.txt'
print(final_file)
with open(final_file, 'a') as f:
f.write(dataset_type + '\n\n')
f.write('dice_train: ' + str(dice_train) + ' dice_valid: ' + str(dice_valid) + ' dice_test: ' + str(dice_test) + '\n\n')
f.write('miou_train: ' + str(miou_train) + ' miou_valid: ' + str(miou_valid) + ' miou_test: ' + str(miou_test) + '\n\n')
f.write('precision_train: ' + str(precision_train) + ' precision_valid: ' + str(precision_valid) + ' precision_test: ' + str(precision_test) + '\n\n')
f.write('recall_train: ' + str(recall_train) + ' recall_valid: ' + str(recall_valid) + ' recall_test: ' + str(recall_test) + '\n\n')
f.write('accuracy_train: ' + str(accuracy_train) + ' accuracy_valid: ' + str(accuracy_valid) + ' accuracy_test: ' + str(accuracy_test) + '\n\n\n\n')
print('File done')
Test/ Val Loss File: etis-laribpolypdb_progress_DuckNet_filters_17_2024-07-12 11:25:19.256742.txt
Result File: results_DuckNet_17_etis-laribpolypdb.txt
CPU: Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz
GPU: NVIDIA A100 80GB
It is really amazing that the U-net structure without using any attention mechanism that can improve the result so much! I can see that this will be a new trend that using multi-viewed field blocks as the connections between features since cfpnet-m.
Thank you for your wonderful work!
I was very impressed with your article, the network you proposed is concise and powerful.
I have a question about the details of network training: Could you please tell me that during the training phase, did you train on these datasets separately, or combined them into one large training set?
Your response would be highly appreciated, and thank you again for your great work!
In the CVC Colondb dataset, the obtained dice_ Test: 0.8902772313306522, why does it differ so much from the results in the paper
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