Generalization error analysis: deep convolutional neural network in mammography

Caleb D. Richter; Ravi K. Samala; Heang-Ping Chan; Lubomir Hadjiiski; Kenny Cha

doi:10.1117/12.2292921

27 February 2018 Generalization error analysis: deep convolutional neural network in mammography

Caleb D. Richter, Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, Kenny Cha

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 1057520 (2018) https://doi.org/10.1117/12.2292921
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

We conducted a study to gain understanding of the generalizability of deep convolutional neural networks (DCNNs) given their inherent capability to memorize data. We examined empirically a specific DCNN trained for classification of masses on mammograms. Using a data set of 2,454 lesions from 2,242 mammographic views, a DCNN was trained to classify masses into malignant and benign classes using transfer learning from ImageNet LSVRC-2010. We performed experiments with varying amounts of label corruption and types of pixel randomization to analyze the generalization error for the DCNN. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) with an N-fold cross validation. Comparisons were made between the convergence times, the inference AUCs for both the training set and the test set of the original image patches without corruption, and the root-mean-squared difference (RMSD) in the layer weights of the DCNN trained with different amounts and methods of corruption. Our experiments observed trends which revealed that the DCNN overfitted by memorizing corrupted data. More importantly, this study improved our understanding of DCNN weight updates when learning new patterns or new labels. Although we used a specific classification task with the ImageNet as example, similar methods may be useful for analysis of the DCNN learning processes, especially those that employ transfer learning for medical image analysis where sample size is limited and overfitting risk is high.

Conference Presentation

Citation Download Citation

Caleb D. Richter, Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, and Kenny Cha "Generalization error analysis: deep convolutional neural network in mammography", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057520 (27 February 2018); https://doi.org/10.1117/12.2292921

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available