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Transfer learning with multiple convolutional neural networks for soft tissue sarcoma MRI classification
Author(s): Haithem Hermessi; Olfa Mourali; Ezzeddine Zagrouba
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Paper Abstract

In this paper, we investigate the classification of two soft tissue sarcoma subtypes within a multi-modal medical dataset based on three pre-trained deep convolutional networks of the ImageNet challenge. We use multiparametric MRI’s with histologically confirmed liposarcoma and leiomyosarcoma. Furthermore, the impact of depth on fine-tuning for medical imaging is highlighted. Therefore, we fine-tune the AlexNet along with deeper architectures of the VGG. Two configurations with 16 and 19 learned layers are fine-tuned. Experimental results reveal a 97.2% of classification accuracy with the AlexNet CNN, while better performance has been achieved using the VGG model with 97.86% and 98.27% on VGG-16-Net and VGG-19-Net, respectively. We demonstrated that depth is favorable for STS subtypes differentiation. Addionally, deeper CNN’s converge faster than shallow, despite, fine-tuned CNN‘s can be used as CAD to help radiologists in decision making.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412L (15 March 2019); doi: 10.1117/12.2522765
Show Author Affiliations
Haithem Hermessi, Univ. de Tunis El Manar (Tunisia)
Olfa Mourali, Univ. de Tunis El Manar (Tunisia)
Ezzeddine Zagrouba, Univ. de Tunis El Manar (Tunisia)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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