Multimodal brain tumor classification

@inproceedings{Lerousseau2020MultimodalBT,
  title={Multimodal brain tumor classification},
  author={Marvin Lerousseau and Eric Deutsch and Nikos Paragios},
  booktitle={BrainLes@MICCAI},
  year={2020}
}
Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathological slides, radiology images should yield additional knowledge towards the efficacy of cancer diagnostics. This work investigates a deep learning method combining whole slide images and magnetic resonance images to classify tumors. In particular, our solution comprises a powerful, generic and modular architecture for… 
Combining Radiology and Pathology for Automatic Glioma Classification
TLDR
An innovative two-stage model to classify gliomas into three subtypes (i.e., glioblastoma, oligodendroglioma, and astrocytoma) based on radiology and histology data is proposed, which could advance multimodal-based glioma research and provide assistance to pathologists and neurologists in diagnosingglioma subtypes.

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