Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
Imon BanerjeeDaniel L Rubin

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.

Citations

Mar 3, 2018·Japanese Journal of Radiology·Koichiro YasakaOsamu Abe
Oct 18, 2019·Pediatric Radiology·Heike Daldrup-Link
May 30, 2020·International Journal of Oncology·Eleftherios TrivizakisKostas Marias
Jun 20, 2020·The British Journal of Radiology·Nicolò GennaroAlexia Francesca Bertuzzi
Feb 3, 2021·European Journal of Nuclear Medicine and Molecular Imaging·Yan-Ran Joyce WangHeike E Daldrup-Link
May 29, 2021·Pediatric Radiology·Jeffrey P OtjenRamesh S Iyer
Dec 16, 2021·JCO Clinical Cancer Informatics·Siddhi RameshMark A Applebaum

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