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A pre-trained model for breast-density classification.
This model is trained using transfer learning on InceptionV3. The model weights were fine tuned using the Mayo Clinic Data. The details of training and data is outlined in https://arxiv.org/abs/2202.08238 . The images should be resampled to a size [299, 299, 3] for training. A training pipeline will be added to the model zoo in near future. The bundle does not support torchscript.
In the folder
sample_data few example input images are stored for each category of images. These images are stored in jpeg format for sharing purpose.
Input and Output Formats
The input image should have the size [299, 299, 3]. For a dicom image which are single channel. The channel can be repeated 3 times. The output is an array with probabilities for each of the four class.
Create a json file with names of all the input files. Execute the following command
python scripts/create_dataset.py -base_dir <path to the bundle root dir>/sample_data -output_file configs/sample_image_data.json
filename for the field
data with the absolute path for
Add scripts folder to your python path as follows
export PYTHONPATH=$PYTHONPATH:<path to the bundle root dir>/scripts
The inference can be executed as follows
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json configs/logging.conf
It is a work in progress and will be shared in the next version soon.
This model is made available from Center for Augmented Intelligence in Imaging, Mayo Clinic Florida. For questions email Vikash Gupta (email@example.com ).
Copyright (c) MONAI Consortium
Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.