An investigation of aggregated transfer learning for classification in digital pathology

T. Rai; A. Morisi; B. Bacci; N. J. Bacon; S. A. Thomas; R. M. La Ragione; M. Bober; K. Wells

doi:10.1117/12.2511754

18 March 2019 An investigation of aggregated transfer learning for classification in digital pathology

T. Rai, A. Morisi, B. Bacci, N. J. Bacon, S. A. Thomas, R. M. La Ragione, M. Bober, K. Wells

Proceedings Volume 10956, Medical Imaging 2019: Digital Pathology; 109560U (2019) https://doi.org/10.1117/12.2511754
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Complex ‘Big Data’ questions that involve machine learning require large datasets for training. This is particularly problematic for Deep Learning methods in the biomedical imaging domain and specifically Digital Pathology. Transfer Learning has been shown to be a promising method for training classifiers on smaller sized datasets. In this work we investigate the effectiveness of aggregated Transfer Learning using VGG19 trained on ImageNet and then fine-tuning parameters with tissue histopathological patches from breast cancer metastatic tissue patches to classify soft tissue sarcoma patches. We compare results with and without transfer learning, and fine tuning applied to different layers. From the results, it is apparent that fine-tuning earlier VGG19 convolutional blocks with breast cancer patches and applying bottleneck feature extraction to soft tissue sarcoma can have an adverse effect on accuracy and other performance measures. Nevertheless, the aggregated approach is a promising method for digital pathology and requires much more investigation.

Citation Download Citation

T. Rai, A. Morisi, B. Bacci, N. J. Bacon, S. A. Thomas, R. M. La Ragione, M. Bober, and K. Wells "An investigation of aggregated transfer learning for classification in digital pathology", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560U (18 March 2019); https://doi.org/10.1117/12.2511754

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