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#CMIMI18#CMIMI18
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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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 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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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