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deep-image-segmentation-for-breast-contour-detection

Implementation of the paper "A novel approach to keypoint detection for the aesthetic evaluation of breast cancer surgery outcomes" by Tiago Gonçalves, Wilson Silva, Maria J. Cardoso and Jaime S. Cardoso.

https://github.com/tiagofilipesousagoncalves/deep-image-segmentation-for-breast-contour-detection

Science Score: 28.0%

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    Links to: arxiv.org
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    Scientific vocabulary similarity
    Low similarity (5.2%) to scientific vocabulary

Keywords

aesthetic-assessment breast-cancer breast-cancer-surgery convolutional-neural-networks deep-learning deep-neural-networks keypoint-detection medical-image-segmentation medical-imaging u-net u-net-plus-plus
Last synced: 6 months ago · JSON representation ·

Repository

Implementation of the paper "A novel approach to keypoint detection for the aesthetic evaluation of breast cancer surgery outcomes" by Tiago Gonçalves, Wilson Silva, Maria J. Cardoso and Jaime S. Cardoso.

Basic Info
  • Host: GitHub
  • Owner: TiagoFilipeSousaGoncalves
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 3.98 MB
Statistics
  • Stars: 2
  • Watchers: 2
  • Forks: 1
  • Open Issues: 11
  • Releases: 0
Topics
aesthetic-assessment breast-cancer breast-cancer-surgery convolutional-neural-networks deep-learning deep-neural-networks keypoint-detection medical-image-segmentation medical-imaging u-net u-net-plus-plus
Created almost 6 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.md

Classification models Zoo

Pretrained classification models for Keras

Models:

| Model | Classes | Weights | No top | Preprocessing| |-----------|:-------:|:----------------------------:|:------:|:------:| | ResNet18 | 1000 | imagenet | + |BGR| | ResNet34 | 1000 | imagenet | + |BGR| | ResNet50 | 1000
11586 |imagenet
imagenet11k-place365ch | + |BGR | | ResNet101 | 1000 | imagenet | + |BGR | | ResNet152 | 1000
11221| imagenet
imagenet11k| + |BGR | | ResNeXt50 | 1000 | imagenet | + |- | | ResNeXt101 | 1000 | imagenet | + |- |

Example

Imagenet inference example:
```python import numpy as np from skimage.io import imread from keras.applications.imagenetutils import decodepredictions

from classificationmodels import ResNet18 from classificationmodels.resnet import preprocess_input

read and prepare image

x = imread('./imgs/tests/seagull.jpg') x = preprocessinput(x, size=(224,224)) x = np.expanddims(x, 0)

load model

model = ResNet18(input_shape=(224,224,3), weights='imagenet', classes=1000)

processing image

y = model.predict(x)

result

print(decode_predictions(y)) ```

Model fine-tuning example: ```python import keras from classification_models import ResNet18

prepare your data

X = ... y = ...

n_classes = 10

build model

basemodel = ResNet18(inputshape=(224,224,3), weights='imagenet', includetop=False) x = keras.layers.AveragePooling2D((7,7))(basemodel.output) x = keras.layers.Dropout(0.3)(x) output = keras.layers.Dense(nclasses)(x) model = keras.models.Model(inputs=[basemodel.input], outputs=[output])

train

model.compile(optimizer='SGD', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(X, y) ```

Owner

  • Name: Tiago Filipe Sousa Gonçalves
  • Login: TiagoFilipeSousaGoncalves
  • Kind: user
  • Location: Porto
  • Company: INESC TEC | FEUP

Science lover, geek culture addicted and a "wanna be" musician.

Citation (CITATION) 

@article{gonccalves2020novel,
  title={A novel approach to keypoint detection for the aesthetic evaluation of breast cancer surgery outcomes},
  author={Gon{\c{c}}alves, Tiago and Silva, Wilson and Cardoso, Maria J and Cardoso, Jaime S},
  journal={Health and Technology},
  volume={10},
  number={4},
  pages={891--903},
  year={2020},
  publisher={Springer}
}

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Dependencies

requirements.txt pypi
  • Keras ==2.3.1
  • Keras-Applications ==1.0.8
  • Keras-Preprocessing ==1.1.0
  • Markdown ==3.1.1
  • Pillow ==6.2.1
  • PyWavelets ==1.1.1
  • PyYAML ==5.1.2
  • Werkzeug ==0.16.0
  • absl-py ==0.8.1
  • astor ==0.8.0
  • cachetools ==3.1.1
  • certifi ==2019.9.11
  • chardet ==3.0.4
  • cycler ==0.10.0
  • decorator ==4.4.1
  • gast ==0.2.2
  • google-auth ==1.7.0
  • google-auth-oauthlib ==0.4.1
  • google-pasta ==0.1.8
  • grpcio ==1.25.0
  • h5py ==2.10.0
  • idna ==2.8
  • imageio ==2.6.1
  • joblib ==0.14.0
  • kiwisolver ==1.1.0
  • matplotlib ==3.1.1
  • networkx ==2.4
  • numpy ==1.17.3
  • oauthlib ==3.1.0
  • opencv-contrib-python ==4.1.1.26
  • opt-einsum ==3.1.0
  • pandas ==0.25.3
  • protobuf ==3.10.0
  • pyasn1 ==0.4.7
  • pyasn1-modules ==0.2.7
  • pyparsing ==2.4.4
  • python-dateutil ==2.8.1
  • pytz ==2019.3
  • requests ==2.22.0
  • requests-oauthlib ==1.2.0
  • rsa ==4.0
  • scikit-image ==0.16.2
  • scikit-learn ==0.21.3
  • scipy ==1.3.1
  • six ==1.13.0
  • tensorboard ==2.0.1
  • tensorflow-estimator ==2.0.1
  • tensorflow-gpu ==2.0.1
  • termcolor ==1.1.0
  • torch ==1.3.1
  • torchvision ==0.4.2
  • urllib3 ==1.25.6
  • wrapt ==1.11.2