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Predicting Breast Nodule Malignancy with Efficient

Convolutional Neural NetworksIan Pan, MA; Alice J. Chu; Yihong Wang, MD, PhD; Derek Merck, PhD; Ana

Lourenco, MD

Warren Alpert Medical SchoolRhode Island Hospital

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Background

BI-RADS is a classification system for breast nodules developed to aid radiologists in determining which suspicious nodules to biopsy (fine-needle aspiration)

Imperfect BI-RAD classification can lead to many unnecessary biopsies

Convolutional Neural Networks have had success in increasing accuracy of prediction in other domains of radiology (chest radiographs, head CT, pediatric hand radiographs)

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Purpose

Improve our predictive ability to discriminate between pathology confirmed benign and malignant breast nodules using ultrasound images trained with convolutional neural networks

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Preprocessing

Grayscale breast ultrasound images collected from Rhode Island Hospital

812 patients total: 279 malignant, 533 benign Ground truth based on cytology from fine needle aspiration Sagittal and transverse view selected for each nodule

Nodule and immediately adjacent surrounding tissue manually cropped out from image to increase signal to noise

Regions of interest resized to 224 x 224 pixels and pixel values scaled to range between -1 and 1

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Breast Nodules

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MobileNet

Efficient CNNs intended for embedded mobile applications

Instantiated with pretrained parameters First trained on 1.2 million color images of

common objects from ImageNet (http://www.image-net.org/) to distinguish among 1,000 categories

Parameters then modified by training CNN on current task of breast nodule malignancy prediction

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Model Training and Evaluation

Modified Double Cross-Validation Scheme (Figure 1) Outer Validation Loop: Data divided into

10 disjoint folds, stratified by malignancy status

Inner Validation Loop: 3 separate training/validation splits with a 90%/10% distribution

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Model Training and Evaluation

Optimization of learning rate, data augmentation probability, dropout probability (Figure 2) 60 different sets of 3 hyperparameter

values were created by sampling each hyperparameter value from prespecified uniform distributions

Models trained for 20 epochs

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Model Training and Evaluation

Models were validated on the validation fold after every epoch

Best performing model across all epochs and hyperparameter iterations according to AUC was selected for evaluation

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Figure 1

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Figure 1

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Figure 2

1 iteration

Dropout (0,1)

Learning Rate (10-3,10-6)

Data Augmentation (0,1)

X 60

20 epochs

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Results

Mean AUC: 0.869 (95% CI: 0.843, 0.895) Median malignancy acores: 0.164 for benign, 0.714 for

malignant Malignancy rates in 5 malignancy score strata with overall

malignancy rate 34.4% 0-0.2: 5.7% 0.2-0.4: 26.2% 0.4-0.6: 42.1% 0.6-0.8: 67.3% 0.8-1.0: 76.8%

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Results

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Discussion and Future Directions

MobileNet CNN accurately predicted malignancy potential for breast ultrasound nodules

With more accurate malignancy prediction, the number of unnecessary biopsies can be reduced At threshold of 0.10, can reduce negative core biopsies

by 40% with 95% sensitivity Future directions:

Larger multi-site datasets Comparison against other CNNs (ResNet50, Inception-V3,

DenseNet) Clinical comparison against accuracy of certified radiologists

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Acknowledgements

Dr. Ana Lourenco Dr. Derek Merck Rhode Island Hospital Department of Diagnostic Imaging Warren Alpert Medical School