MIDL 2021, I4, Barhoumi et. al., Short Oral
Channel: MIDL Society
Date: 05/08/2021

I4 - Scopeformer: n-CNN-ViT hybrid model for Intracranial hemorrhage subtypes classification

Yassine Barhoumi, Ghulam Rasool

Conference page: https://2021.midl.io/papers/I4

Abstract: We propose a feature generator backbone composed of an ensemble of convolutional neural networks (CNNs) to improve the recently emerging Vision Transformer (ViT) models. We tackled the RSNA intracranial hemorrhage classification problem, i.e., identifying various hemorrhage  types  from  computed  tomography  (CT)  slices.   We  show  that  by gradually stacking  several  feature  maps  extracted  using  multiple  Xception  CNNs,  we  can develop a  feature-rich  input  for  the  ViT  model.   Our  approach  allowed  the  ViT  model  to  pay attention to relevant features at multiple levels.  Moreover, pretraining the ”n” CNNs using various paradigms leads to a diverse feature set and further improves the performance of the proposed n-CNN-ViT. We achieved a test accuracy of 98.04% with a weighted logarithmic loss value of 0.0708.  The proposed architecture is modular and scalable in both the number of CNNs used for feature extraction and the size of the ViT.