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An investigation of aggregated transfer learning for classification in digital pathology
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Paper 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.

Paper Details

Date Published: 18 March 2019
PDF: 8 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560U (18 March 2019); doi: 10.1117/12.2511754
Show Author Affiliations
T. Rai, Ctr. for Vision Speech and Signal Processing, Univ. of Surrey (United Kingdom)
A. Morisi, School of Veterinary Medicine, Univ. of Surrey (United Kingdom)
B. Bacci, School of Veterinary Medicine, Univ. of Surrey (United Kingdom)
N. J. Bacon, Fitzpatrick Referrals (United Kingdom)
S. A. Thomas, National Physical Lab. (United Kingdom)
R. M. La Ragione, School of Veterinary Medicine, Univ. of Surrey (United Kingdom)
M. Bober, Ctr. for Vision Speech and Signal Processing, Univ. of Surrey (United Kingdom)
K. Wells, Ctr. for Vision Speech and Signal Processing, Univ. of Surrey (United Kingdom)


Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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