sam186 / end2end-all-conv

End-to-end training for breast cancer diagnosis using deep all convolutional networks

End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design

Li Shen, Ph.D. CS

Icahn School of Medicine at Mount Sinai

New York, New York, USA

Introduction

This is the companion site for our paper - "End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design" at arXiv. Access the paper here.

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.

Model downloads

A few best models are available for downloading at this Google Drive folder. 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
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.

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.

Let the Python modules be seen

I simpy modify the PYTHONPATH variable like this: export PYTHONPATH=$PYTHONPATH:your_path_to_repo/end2end-all-conv

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.