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Cross-view transformers for multi-view analysis of unregistered medical images

This code accompanies the paper

Multi-view analysis of unregistered medical images using cross-view-transformers
by Gijs van Tulder, Yao Tong, Elena Marchiori
from the Data Science Group, Faculty of Science, Radboud University, Nijmegen, the Netherlands
Presented at MICCAI 2021.

The paper is available at

The most recent version of this code is available at https://vantulder.net/code/2021/miccai-transformers/

Description

This code implements cross-view transformers and the baseline networks evaluated in the paper.

Structure

  • The main directory contains the Python code for the models, training, and evaluation.
  • The experiments/ directory contains bash scripts with the experimental settings used in the paper.
  • The paper-tables/ directory contains results and scripts to generate the tables for the paper.
  • The data-scripts-ddsm/ directory contains the scripts to preprocess the CBIS-DDSM images.
  • The data-scripts-chexpert/ directory contains the scripts to preprocess the CheXpert images.

Requirements

  • The experiments were run on Python 3.6.9 with PyTorch 1.7.0.
  • The data from the CBIS-DDSM and CheXpert datasets is not included here, but can be downloaded elsewhere.

Copyright and license

Copyright (C) 2021 Gijs van Tulder / Radboud University, the Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.