Deep Learning to Improve Breast Cancer Detection on Screening Mammography

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Deep Learning machine-learning deep-learning cancer-imaging-research
Overview

Shield: CC BY-NC-SA 4.0

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

CC BY-NC-SA 4.0

Deep Learning to Improve Breast Cancer Detection on Screening Mammography (End-to-end Training for Whole Image Breast Cancer Screening using An All Convolutional Design)

Li Shen, Ph.D. CS

Icahn School of Medicine at Mount Sinai

New York, New York, USA

Fig1

Introduction

This is the companion site for our paper that was originally titled "End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design" and was retitled as "Deep Learning to Improve Breast Cancer Detection on Screening Mammography". The paper has been published here. You may also find the arXiv version here. This work was initially presented at the NIPS17 workshop on machine learning for health. Access the 4-page short paper here. Download the poster.

For our entry in the DREAM2016 Digital Mammography challenge, see this write-up. This work is much improved from our method used in the challenge.

Whole image model downloads

A few best whole image models are available for downloading at this Google Drive folder. YaroslavNet is the DM challenge top-performing team's method. Here is a table for individual downloads:

Database Patch Classifier Top Layers (two blocks) Single AUC Augmented AUC Link
DDSM Resnet50 [512-512-1024]x2 0.86 0.88 download
DDSM VGG16 512x1 0.83 0.86 download
DDSM VGG16 [512-512-1024]x2 0.85 0.88 download
DDSM YaroslavNet heatmap + max pooling + FC16-8 + shortcut 0.83 0.86 download
INbreast VGG16 512x1 0.92 0.94 download
INbreast VGG16 [512-512-1024]x2 0.95 0.96 download
  • Inference level augmentation is obtained by horizontal and vertical flips to generate 4 predictions.
  • The listed scores are single model AUC and prediction averaged AUC.
  • 3 Model averaging on DDSM gives AUC of 0.91
  • 2 Model averaging on INbreast gives AUC of 0.96.

Patch classifier model downloads

Several patch classifier models (i.e. patch state) are also available for downloading at this Google Drive folder. Here is a table for individual download:

Model Train Set Accuracy Link
Resnet50 S10 0.89 download
VGG16 S10 0.84 download
VGG19 S10 0.79 download
YaroslavNet (Final) S10 0.89 download
Resnet50 S30 0.91 download
VGG16 S30 0.86 download
VGG19 S30 0.89 download

With patch classifier models, you can convert them into any whole image classifier by adding convolutional, FC and heatmap layers on top and see for yourself.

A bit explanation of this repository's file structure

  • The .py files under the root directory are Python modules to be imported.
  • You shall set the PYTHONPATH variable like this: export PYTHONPATH=$PYTHONPATH:your_path_to_repos/end2end-all-conv so that the Python modules can be imported.
  • The code for patch sampling, patch classifier and whole image training are under the ddsm_train folder.
  • sample_patches_combined.py is used to sample patches from images and masks.
  • patch_clf_train.py is used to train a patch classifier.
  • image_clf_train.py is used to train a whole image classifier, either on top of a patch classifier or from another already trained whole image classifier (i.e. finetuning).
  • There are multiple shell scripts under the ddsm_train folder to serve as examples.

Some input files' format

I've got a lot of requests asking about the format of some input files. Here I provide the first few lines and hope they can be helpful:

roi_mask_path.csv

patient_id,side,view,abn_num,pathology,type
P_00005,RIGHT,CC,1,MALIGNANT,calc
P_00005,RIGHT,MLO,1,MALIGNANT,calc
P_00007,LEFT,CC,1,BENIGN,calc
P_00007,LEFT,MLO,1,BENIGN,calc
P_00008,LEFT,CC,1,BENIGN_WITHOUT_CALLBACK,calc

pat_train.txt

P_00601
P_00413
P_01163
P_00101
P_01122

Transfer learning is as easy as 1-2-3

In order to transfer a model to your own data, follow these easy steps.

Determine the rescale factor

The rescale factor is used to rescale the pixel intensities so that the max value is 255. For PNG format, the max value is 65535, so the rescale factor is 255/65535 = 0.003891. If your images are already in the 255 scale, set rescale factor to 1.

Calculate the pixel-wise mean

This is simply the mean pixel intensity of your train set images.

Image size

This is currently fixed at 1152x896 for the models in this study. However, you can change the image size when converting from a patch classifier to a whole image classifier.

Finetune

Now you can finetune a model on your own data for cancer predictions! You may check out this shell script. Alternatively, copy & paste from here:

TRAIN_DIR="INbreast/train"
VAL_DIR="INbreast/val"
TEST_DIR="INbreast/test"
RESUME_FROM="ddsm_vgg16_s10_[512-512-1024]x2_hybrid.h5"
BEST_MODEL="INbreast/transferred_inbreast_best_model.h5"
FINAL_MODEL="NOSAVE"
export NUM_CPU_CORES=4

python image_clf_train.py \
    --no-patch-model-state \
    --resume-from $RESUME_FROM \
    --img-size 1152 896 \
    --no-img-scale \
    --rescale-factor 0.003891 \
    --featurewise-center \
    --featurewise-mean 44.33 \
    --no-equalize-hist \
    --batch-size 4 \
    --train-bs-multiplier 0.5 \
    --augmentation \
    --class-list neg pos \
    --nb-epoch 0 \
    --all-layer-epochs 50 \
    --load-val-ram \
    --load-train-ram \
    --optimizer adam \
    --weight-decay 0.001 \
    --hidden-dropout 0.0 \
    --weight-decay2 0.01 \
    --hidden-dropout2 0.0 \
    --init-learningrate 0.0001 \
    --all-layer-multiplier 0.01 \
    --es-patience 10 \
    --auto-batch-balance \
    --best-model $BEST_MODEL \
    --final-model $FINAL_MODEL \
    $TRAIN_DIR $VAL_DIR $TEST_DIR

Some explanations of the arguments:

  • The batch size for training is the product of --batch-size and --train-bs-multiplier. Because training uses roughtly twice (both forward and back props) the GPU memory of testing, --train-bs-multiplier is set to 0.5 here.
  • For model finetuning, only the second stage of the two-stage training is used here. So --nb-epoch is set to 0.
  • --load-val-ram and --load-train-ram will load the image data from the validation and train sets into memory. You may want to turn off these options if you don't have sufficient memory. When turned off, out-of-core training will be used.
  • --weight-decay and --hidden-dropout are for stage 1. --weight-decay2 and --hidden-dropout2 are for stage 2.
  • The learning rate for stage 1 is --init-learningrate. The learning rate for stage 2 is the product of --init-learningrate and --all-layer-multiplier.

Computational environment

The research in this study is carried out on a Linux workstation with 8 CPU cores and a single NVIDIA Quadro M4000 GPU with 8GB memory. The deep learning framework is Keras 2 with Tensorflow as the backend.

About Keras version

It is known that Keras >= 2.1.0 can give errors due an API change. See issue #7. Use Keras with version < 2.1.0. For example, Keras=2.0.8 is known to work.

TERMS OF USE

All data is free to use for non-commercial purposes. For commercial use please contact MSIP.

Comments
  • How do I find

    How do I find ".csv" file?

    Hello~lishen, I have download the CBIS-DDSM dataset from The Cancer Imaging Archive, it's show in the picture: image But it havn't the ".csv" file，does this file need to be created by ourselves? But we just know the information about the patient, side, CC or MLO, there's no more information. As shown in the above picture, the test and train sets are scattered throughout the folder, and they contain many subfolders, I don't know how to set the parameter of "train_dir", "test_dir", "val_dir".

    So~I have two requests: 1:Would you please provide the ".csv" file? 2:Would you tell us, does we need to sort out the data by ourselves and integrate all the images into the "test_set", "train_set", "val_set" three folders?

    Thank you for your time, best wishes for you!

    opened by wurenzhong 16
  • Too many values to unpack in calculation step

    Too many values to unpack in calculation step

    Hey, thank you for this work. I have been trying to reproduce the results with Calc_Test and Mass_Test but at the calculation step I keep getting the following error. Can you suggest what I might be doing wrong here. screenshot from 2018-07-04 17-04-14

    opened by nerdykamil 6
  • Our miccai 2017 paper also works for the whole mammogram classification.

    Our miccai 2017 paper also works for the whole mammogram classification.

    Hi Shen,

    I got your email for your paper. Our MICCAI 2017 paper proposes several schemes for whole mammogram classification.

    Zhu, Wentao, Qi Lou, Yeeleng Scott Vang, and Xiaohui Xie. "Deep multi-instance networks with sparse label assignment for whole mammogram classification." MICCAI (2017).

    Thanks! Wentao

    opened by wentaozhu 5
  • What's the order of the patch classifier predictions?

    What's the order of the patch classifier predictions?

    Is it noted in the paper, that the patch classifiers are all trained to predict the following classes: background, malignant mass, benign mass, malignant calcification and benign calcification. It seem consistent with the model prediction outputs of the patch classifiers in the Google Drive, which has a size of 5 output classes.

    But how do we check which class each of the patch model outputs are associated with? I.e. what is the ordering of the outputs.

    opened by frfa1 4
  • ValueError when model testing

    ValueError when model testing

    I encountered the following error ValueError: not enough values to unpack (expected 3, got 2) when accessing
    index_array, current_index, current_batch_size = next(self.index_generator) in line 1203 in dm_image.py

    This happens in both fine tuning phase and model testing phase (as executing in "Example_model_test.ipynb" notebook inside ddsm_train folder)

    Is there anyone also encountered this issue and resolved it?

    opened by genuinwix 1
  • Good comment

    Good comment

    Hi Li,

    Well done on your Ph.D. thesis! I skimmed through your paper and saw it is very similar to a friend of mine's (@Adamouization) Master's dissertation.

    Just a quick comment to say good luck for the future and well done!

    opened by Anri-Lombard 0
  • predict result means?

    predict result means?

    i use the ddsm_vgg16_s10_[512-512-1024]x2_hybrid.h5 model to predict my own data,the result just like this,i want to know which one is the benign probability? 0.2 or 0.8?

    [[0.2 0.8]]

    opened by picEmily 0
  • Images used?

    Images used?

    This is a great repo! I'm wondering if you have the images you used. I loaded one of the models and during inference I scale my new images to be 0-255 then shift by -44.33 (mean during training according to https://git.io/JeO7x). But i'm not sure its working as well as it should, so it would be easier if I can probe it with images used during training. Thanks!

    opened by aliabd 0
  • Unclear Pretrained steps

    Unclear Pretrained steps

    In the publication https://www.nature.com/articles/s41598-019-48995-4 you mentioned the 3-stage training strategy ment top layer number is set to "46" for Resnet50.

    I saw not documentation of this here. By 46, are you reffering to 'res3a_branch1' (deduced from net.layers[46].name)? What criteria was used to settle on this layer?

    tnx.

    opened by GNovich 0
  • Image preprocessing - convert to PNG, downscale

    Image preprocessing - convert to PNG, downscale

    Hi, would it be possible to receive some code that performs the image pre-processing? I'm aware some of it is using ImageMagick via the Linux command line, but I cannot correctly sort this at present. edit: I am trying to use the CBIS-DDSM dataset in the same way, using the patch classifier. Not necessarily the same splits; just the pre-processing. Kind regards.

    opened by priley95 1
  • Too Large Dataset

    Too Large Dataset

    Hi all,

    Is there anyone who is having only small set from the original dataset?? (Because original dataset is about 163 GB of size)

    I just need small set of data from that dataset.

    Thanks

    opened by genuinwix 0
  • Preprocessed dataset

    Preprocessed dataset

    Hi, thanks to your great work. have you put your pre-processed dataset of DDSM in google drive ? I will be very happy if you help me to preprocess CBIS-DDSM dataset. thanks a lot.

    opened by MahdiHeybati 1
  • Patch Classifier testing results in 0.25 AUC

    Patch Classifier testing results in 0.25 AUC

    Hello Shen, first congratulations on the great effort. I have tested two of your patch models on 400 jpg patches from CBIS-DDSM patches but I have gained an accuracy of 0.25 I have 100 images per class (calcification benign - calcification malignant - mass benign - mass malignant), so I was wondering what is the wrong that I am doing in the prediction?

    test_generator = test_imgen.flow_from_directory(
                                                     directory=test_dir,
                                                     target_size=(500, 500),
                                                     color_mode="rgb",
                                                     batch_size=1,
                                                     class_mode=None,
                                                     shuffle=False)
test_generator.reset()
model = load_model("s30_resnet50.h5")
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
pred=model.predict_generator(test_generator,verbose=1,steps=nb_test_samples)
predicted_class_indices=np.argmax(pred,axis=1)```
    opened by MohammedAdelFahmi 0
Owner
Li Shen
I'm an academic researcher with many years of experience developing machine learning algorithms and bioinformatic software and analyzing genomic data.
Li Shen
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