@article{BengsPantBockmayrSchüllerSchlaefer+2021+63+66,
author = {M. Bengs and S. Pant and M. Bockmayr and U. Schüller and A. Schlaefer},
title = {Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning.},
journal = {Current Directions in Biomedical Engineering.},
year = {2021},
volume = {7.},
number = {(1),},
pages = {63-66},
doi = {doi:10.1515/cdbme-2021-1014},
url = {https://doi.org/10.1515/cdbme-2021-1014},
abstract = {Medulloblastoma   (MB)   is   a   primary   central nervous system tumor and the most common malignant brain cancer among children. Neuropathologists perform microscopic inspection of histopathological tissue slides under a microscope to assess the severity of the tumor. This is a time-consuming  task  and  often  infused  with  observer  variability. Recently,  pre-trained  convolutional  neural  networks  (CNN) have  shown  promising results  for  MB subtype  classification. Typically, high-resolution images are divided into smaller tiles for  classification,  while  the  size  of  the  tiles  has  not  been systematically evaluated. We study the impact of tile size and input  strategy  and  classify  the  two  major  histopathological subtypes—Classic and Desmoplastic/Nodular. To this end, we use  recently  proposed  EfficientNets  and  evaluate  tiles  with increasing  size  combined  with  various  downsampling  scales. Our results demonstrate using large input tiles pixels followed by     intermediate     downsampling     and     patch     cropping significantly  improves  MB  classification  performance.  Our top-performing  method  achieves  the  AUC-ROC  value  of 90.90% compared to 84.53% using the previous approach with smaller input tiles}
}

@COMMENT{Bibtex file generated on 2026-5-28 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/mtec/publications/2024-2020 }