Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. (April 2018)

• Record Type: Journal Article
• Authors: Banerjee, Imon; Crawley, Alexis; Bhethanabotla, Mythili; Daldrup-Link, Heike E; Rubin, Daniel L.
• Is Part Of: Computerized medical imaging and graphics. Volume 65(2018)
• Page Start: 167
• Page End: 175
• Publication Date: 2018-04
• Subjects: Rhabdomyosarcoma -- Computer aided diagnosis -- Image fusion -- Transfer learning -- Deep neural networks; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2017.05.002
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: A deep-learning based CADx for diagnosis of embryonal and alveolar subtypes. Diagnosis has been performed by solely by analyzing multiparametric MR images. Created a fusion of diffusion-weighted and T1-weighted MR scans. A pre-trained deep neural network has been fine-tuned based on the fused images. Achieved 85% cross validation accuracy for classifying the two RMS subtypes. The system can provide an efficient and reproducible diagnosis with less human interaction. Abstract: This paper presents a deep-learning-based CADx for the differential diagnosis of embryonal (ERMS) and alveolar (ARMS) subtypes of rhabdomysarcoma (RMS) solely by analyzing multiparametric MR images. We formulated an automated pipeline that creates a comprehensive representation of tumor by performing a fusion of diffusion-weighted MR scans (DWI) and gadolinium chelate-enhanced T1−weighted MR scans (MRI). Finally, we adapted transfer learning approach where a pre-trained deep convolutional neural network has been fine-tuned based on the fused images for performing classification of the two RMS subtypes. We achieved 85% cross validation prediction accuracy from the fine-tuned deep CNN model. Our system can be exploited to provide a fast, efficient and reproducible diagnosis of RMS subtypes with less human interaction. The framework offers an efficient integration between advanced image processing methods and cutting-edge deep learning techniques which can be extended to deal with other clinical domains that involve multimodal imaging for disease diagnosis.